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• Data Quality and Master Data Management Resources

  - by Dejan Sarka

  Many companies or organizations do regular data cleansing. When you cleanse the data, the data quality goes up to some higher level. The data quality level is determined by the amount of work invested in the cleansing. As time passes, the data quality deteriorates, and you need to repeat the cleansing process. If you spend an equal amount of effort as you did with the previous cleansing, you can expect the same level of data quality as you had after the previous cleansing. And then the data quality deteriorates over time again, and the cleansing process starts over and over again. The idea of Data Quality Services is to mitigate the cleansing process. While the amount of time you need to spend on cleansing decreases, you will achieve higher and higher levels of data quality. While cleansing, you learn what types of errors to expect, discover error patterns, find domains of correct values, etc. You don’t throw away this knowledge. You store it and use it to find and correct the same issues automatically during your next cleansing process. The following figure shows this graphically. The idea of master data management, which you can perform with Master Data Services (MDS), is to prevent data quality from deteriorating. Once you reach a particular quality level, the MDS application—together with the defined policies, people, and master data management processes—allow you to maintain this level permanently. This idea is shown in the following picture. OK, now you know what DQS and MDS are about. You can imagine the importance on maintaining the data quality. Here are some resources that help you preparing and executing the data quality (DQ) and master data management (MDM) activities. Books Dejan Sarka and Davide Mauri: Data Quality and Master Data Management with Microsoft SQL Server 2008 R2 – a general introduction to MDM, MDS, and data profiling. Matching explained in depth. Dejan Sarka, Matija Lah and Grega Jerkic: MCTS Self-Paced Training Kit (Exam 70-463): Building Data Warehouses with Microsoft SQL Server 2012 – I wrote quite a few chapters about DQ and MDM, and introduced also SQL Server 2012 DQS. Thomas Redman: Data Quality: The Field Guide – you should start with this book. Thomas Redman is the father of DQ and MDM. Tyler Graham: Microsoft SQL Server 2012 Master Data Services – MDS in depth from a product team mate. Arkady Maydanchik: Data Quality Assessment – data profiling in depth. Tamraparni Dasu, Theodore Johnson: Exploratory Data Mining and Data Cleaning – advanced data profiling with data mining. Forthcoming presentations I am presenting a DQS and MDM seminar at PASS SQL Rally Amsterdam 2013: Wednesday, November 6th, 2013: Enterprise Information Management with SQL Server 2012 – a good kick start to your first DQ and / or MDM project. Courses Data Quality and Master Data Management with SQL Server 2012 – I wrote a 2-day course for SolidQ. If you are interested in this course, which I could also deliver in a shorter seminar way, you can contact your closes SolidQ subsidiary, or, of course, me directly on addresses [email protected] or [email protected]. This course could also complement the existing courseware portfolio of training providers, which are welcome to contact me as well. Start improving the quality of your data now!

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• Granular Clipboard Control in Oracle IRM

  - by martin.abrahams

  One of the main leak prevention controls that customers are looking for is clipboard control. After all, there is little point in controlling access to a document if authorised users can simply make unprotected copies by use of the cut and paste mechanism. Oddly, for such a fundamental requirement, many solutions only offer very simplistic clipboard control - and require the customer to make an awkward choice between usability and security. In many cases, clipboard control is simply an ON-OFF option. By turning the clipboard OFF, you disable one of the most valuable edit functions known to man. Try working for any length of time without copying and pasting, and you'll soon appreciate how valuable that function is. Worse, some solutions disable the clipboard completely - not just for the protected document but for all of the various applications you have open at the time. Normal service is only resumed when you close the protected document. In this way, policy enforcement bleeds out of the particular assets you need to protect and interferes with the entire user experience. On the other hand, turning the clipboard ON satisfies a fundamental usability requirement - but also makes it really easy for users to create unprotected copies of sensitive information, maliciously or otherwise. All they need to do is paste into another document. If creating unprotected copies is this simple, you have to question how much you are really gaining by applying protection at all. You may not be allowed to edit, forward, or print the protected asset, but all you need to do is create a copy and work with that instead. And that activity would not be tracked in any way. So, a simple ON-OFF control creates a real tension between usability and security. If you are only using IRM on a small scale, perhaps security can outweigh usability - the business can put up with the restriction if it only applies to a handful of important documents. But try extending protection to large numbers of documents and large user communities, and the restriction rapidly becomes really unwelcome. I am aware of one solution that takes a different tack. Rather than disable the clipboard, pasting is always permitted, but protection is automatically applied to any document that you paste into. At first glance, this sounds great - protection travels with the content. However, at any scale this model may not be so appealing once you've had to deal with support calls from users who have accidentally applied protection to documents that really don't need it - which would be all too easily done. This may help control leakage, but it also pollutes the system with documents that have policies applied with no obvious rhyme or reason, and it can seriously inconvenience the business by making non-sensitive documents difficult to access. And what policy applies if you paste some protected content into an already protected document? Which policy applies? There are no prizes for guessing that Oracle IRM takes a rather different approach. The Oracle IRM Approach Oracle IRM offers a spectrum of clipboard controls between the extremes of ON and OFF, and it leverages the classification-based rights model to give granular control that satisfies both security and usability needs. Firstly, we take it for granted that if you have EDIT rights, of course you can use the clipboard within a given document. Why would we force you to retype a piece of content that you want to move from HERE... to HERE...? If the pasted content remains in the same document, it is equally well protected whether it be at the beginning, middle, or end - or all three. So, the first point is that Oracle IRM always enables the clipboard if you have the right to edit the file. Secondly, whether we enable or disable the clipboard, we only affect the protected document. That is, you can continue to use the clipboard in the usual way for unprotected documents and applications regardless of whether the clipboard is enabled or disabled for the protected document(s). And if you have multiple protected documents open, each may have the clipboard enabled or disabled independently, according to whether you have Edit rights for each. So, even for the simplest cases - the ON-OFF cases - Oracle IRM adds value by containing the effect to the protected documents rather than to the whole desktop environment. Now to the granular options between ON and OFF. Thanks to our classification model, we can define rights that enable pasting between documents in the same classification - ie. between documents that are protected by the same policy. So, if you are working on this month's financial report and you want to pull some data from last month's report, you can simply cut and paste between the two documents. The two documents are classified the same way, subject to the same policy, so the content is equally safe in both documents. However, if you try to paste the same data into an unprotected document or a document in a different classification, you can be prevented. Thus, the control balances legitimate user requirements to allow pasting with legitimate information security concerns to keep data protected. We can take this further. You may have the right to paste between related classifications of document. So, the CFO might want to copy some financial data into a board document, where the two documents are sealed to different classifications. The CFO's rights may well allow this, as it is a reasonable thing for a CFO to want to do. But policy might prevent the CFO from copying the same data into a classification that is accessible to external parties. The above option, to copy between classifications, may be for specific classifications or open-ended. That is, your rights might enable you to go from A to B but not to C, or you might be allowed to paste to any classification subject to your EDIT rights. As for so many features of Oracle IRM, our classification-based rights model makes this type of granular control really easy to manage - you simply define that pasting is permitted between classifications A and B, but omit C. Or you might define that pasting is permitted between all classifications, but not to unprotected locations. The classification model enables millions of documents to be controlled by a few such rules. Finally, you MIGHT have the option to paste anywhere - such that unprotected copies may be created. This is rare, but a legitimate configuration for some users, some use cases, and some classifications - but not something that you have to permit simply because the alternative is too restrictive. As always, these rights are defined in user roles - so different users are subject to different clipboard controls as required in different classifications. So, where most solutions offer just two clipboard options - ON-OFF or ON-but-encrypt-everything-you-touch - Oracle IRM offers real granularity that leverages our classification model. Indeed, I believe it is the lack of a classification model that makes such granularity impractical for other IRM solutions, because the matrix of rules for controlling pasting would be impossible to manage - there are so many documents to consider, and more are being created all the time.

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• Big Data – Learning Basics of Big Data in 21 Days – Bookmark

  - by Pinal Dave

  Earlier this month I had a great time to write Bascis of Big Data series. This series received great response and lots of good comments I have received, I am going to follow up this basics series with further in-depth series in near future. Here is the consolidated blog post where you can find all the 21 days blog posts together. Bookmark this page for future reference. Big Data – Beginning Big Data – Day 1 of 21 Big Data – What is Big Data – 3 Vs of Big Data – Volume, Velocity and Variety – Day 2 of 21 Big Data – Evolution of Big Data – Day 3 of 21 Big Data – Basics of Big Data Architecture – Day 4 of 21 Big Data – Buzz Words: What is NoSQL – Day 5 of 21 Big Data – Buzz Words: What is Hadoop – Day 6 of 21 Big Data – Buzz Words: What is MapReduce – Day 7 of 21 Big Data – Buzz Words: What is HDFS – Day 8 of 21 Big Data – Buzz Words: Importance of Relational Database in Big Data World – Day 9 of 21 Big Data – Buzz Words: What is NewSQL – Day 10 of 21 Big Data – Role of Cloud Computing in Big Data – Day 11 of 21 Big Data – Operational Databases Supporting Big Data – RDBMS and NoSQL – Day 12 of 21 Big Data – Operational Databases Supporting Big Data – Key-Value Pair Databases and Document Databases – Day 13 of 21 Big Data – Operational Databases Supporting Big Data – Columnar, Graph and Spatial Database – Day 14 of 21 Big Data – Data Mining with Hive – What is Hive? – What is HiveQL (HQL)? – Day 15 of 21 Big Data – Interacting with Hadoop – What is PIG? – What is PIG Latin? – Day 16 of 21 Big Data – Interacting with Hadoop – What is Sqoop? – What is Zookeeper? – Day 17 of 21 Big Data – Basics of Big Data Analytics – Day 18 of 21 Big Data – How to become a Data Scientist and Learn Data Science? – Day 19 of 21 Big Data – Various Learning Resources – How to Start with Big Data? – Day 20 of 21 Big Data – Final Wrap and What Next – Day 21 of 21 Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• Getting a Database into Source Control

  - by Grant Fritchey

  For any number of reasons, from simple auditing, to change tracking, to automated deployment, to integration with application development processes, you’re going to want to place your database into source control. Using Red Gate SQL Source Control this process is extremely simple. SQL Source Control works within your SQL Server Management Studio (SSMS) interface.  This means you can work with your databases in any way that you’re used to working with them. If you prefer scripts to using the GUI, not a problem. If you prefer using the GUI to having to learn T-SQL, again, that’s fine. After installing SQL Source Control, this is what you’ll see when you open SSMS:   SQL Source Control is now a direct piece of the SSMS environment. The key point initially is that I currently don’t have a database selected. You can even see that in the SQL Source Control window where it shows, in red, “No database selected – select a database in Object Explorer.” If I expand my Databases list in the Object Explorer, you’ll be able to immediately see which databases have been integrated with source control and which have not. There are visible differences between the databases as you can see here:   To add a database to source control, I first have to select it. For this example, I’m going to add the AdventureWorks2012 database to an instance of the SVN source control software (I’m using uberSVN). When I click on the AdventureWorks2012 database, the SQL Source Control screen changes:   I’m going to need to click on the “Link database to source control” text which will open up a window for connecting this database to the source control system of my choice.  You can pick from the default source control systems on the left, or define one of your own. I also have to provide the connection string for the location within the source control system where I’ll be storing my database code. I set these up in advance. You’ll need two. One for the main set of scripts and one for special scripts called Migrations that deal with different kinds of changes between versions of the code. Migrations help you solve problems like having to create or modify data in columns as part of a structural change. I’ll talk more about them another day. Finally, I have to determine if this is an isolated environment that I’m going to be the only one use, a dedicated database. Or, if I’m sharing the database in a shared environment with other developers, a shared database.  The main difference is, under a dedicated database, I will need to regularly get any changes that other developers have made from source control and integrate it into my database. While, under a shared database, all changes for all developers are made at the same time, which means you could commit other peoples work without proper testing. It all depends on the type of environment you work within. But, when it’s all set, it will look like this: SQL Source Control will compare the results between the empty folders in source control and the database, AdventureWorks2012. You’ll get a report showing exactly the list of differences and you can choose which ones will get checked into source control. Each of the database objects is scripted individually. You’ll be able to modify them later in the same way. Here’s the list of differences for my new database:   You can select/deselect all the objects or each object individually. You also get a report showing the differences between what’s in the database and what’s in source control. If there was already a database in source control, you’d only see changes to database objects rather than every single object. You can see that the database objects can be sorted by name, by type, or other choices. I’m going to add a comment such as “Initial creation of database in source control.” And then click on the Commit button which will put all the objects in my database into the source control system. That’s all it takes to get the objects into source control initially. Now is when things can get fun with breaking changes to code, automated deployments, unit testing and all the rest.

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• Data Quality Through Data Governance

  Data Quality Governance Data quality is very important to every organization, bad data cost an organization time, money, and resources that could be prevented if the proper governance was put in to place.  Data Governance Program Criteria: Support from Executive Management and all Business Units Data Stewardship Program  Cross Functional Team of Data Stewards Data Governance Committee Quality Structured Data It should go without saying but any successful project in today’s business world must get buy in from executive management and all stakeholders involved with the project. If management does not fully support a project because they see it is in there and the company’s best interest then they will remove/eliminate funding, resources and allocated time to work on the project. In essence they can render a project dead until it is official killed by the business. In addition, buy in from stake holders is also very important because they can cause delays increased spending in time, money and resources because they do not support a project. Data Stewardship programs are administered by a data steward manager who primary focus is to support, train and manage a cross functional data stewards team. A cross functional team of data stewards are pulled from various departments act to ensure that all systems work to ensure that an organization’s goals are achieved. Typically, data stewards are subject matter experts that act as mediators between their respective departments and IT. Data Quality Procedures Data Governance Committees are composed of data stewards, Upper management, IT Leadership and various subject matter experts depending on a company. The primary goal of this committee is to define strategic goals, coordinate activities, set data standards and offer data guidelines for the business. Data Quality Policies In 1997, Claudia Imhoff defined a Data Stewardship’s responsibility as to approve business naming standards, develop consistent data definitions, determine data aliases, develop standard calculations and derivations, document the business rules of the corporation, monitor the quality of the data in the data warehouse, define security requirements, and so forth. She further explains data stewards responsible for creating and enforcing polices on the following but not limited to issues. Resolving Data Integration Issues Determining Data Security Documenting Data Definitions, Calculations, Summarizations, etc. Maintaining/Updating Business Rules Analyzing and Improving Data Quality

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• SQLAuthority News – Links to Book On Line – Data Mining Algorithms (Analysis Services – Data Mining)

  - by pinaldave

  I have quite often received request for the Data Mining Algorithms details. Book Online has wonderful resources for the same. I suggest to read them here. Data Mining Algorithms (Analysis Services – Data Mining) The data mining algorithm is the mechanism that creates a data mining model. To create a model, an algorithm first analyzes a set [...]

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• SQL SERVER – Advanced Data Quality Services with Melissa Data – Azure Data Market

  - by pinaldave

  There has been much fanfare over the new SQL Server 2012, and especially around its new companion product Data Quality Services (DQS). Among the many new features is the addition of this integrated knowledge-driven product that enables data stewards everywhere to profile, match, and cleanse data. In addition to the homegrown rules that data stewards can design and implement, there are also connectors to third party providers that are hosted in the Azure Datamarket marketplace.  In this review, I leverage SQL Server 2012 Data Quality Services, and proceed to subscribe to a third party data cleansing product through the Datamarket to showcase this unique capability. Crucial Questions For the purposes of the review, I used a database I had in an Excel spreadsheet with name and address information. Upon a cursory inspection, there are miscellaneous problems with these records; some addresses are missing ZIP codes, others missing a city, and some records are slightly misspelled or have unparsed suites. With DQS, I can easily add a knowledge base to help standardize my values, such as for state abbreviations. But how do I know that my address is correct? And if my address is not correct, what should it be corrected to? The answer lies in a third party knowledge base by the acknowledged USPS certified address accuracy experts at Melissa Data. Reference Data Services Within DQS there is a handy feature to actually add reference data from many different third-party Reference Data Services (RDS) vendors. DQS simplifies the processes of cleansing, standardizing, and enriching data through custom rules and through service providers from the Azure Datamarket. A quick jump over to the Datamarket site shows me that there are a handful of providers that offer data directly through Data Quality Services. Upon subscribing to these services, one can attach a DQS domain or composite domain (fields in a record) to a reference data service provider, and begin using it to cleanse, standardize, and enrich that data. Besides what I am looking for (address correction and enrichment), it is possible to subscribe to a host of other services including geocoding, IP address reference, phone checking and enrichment, as well as name parsing, standardization, and genderization.  These capabilities extend the data quality that DQS has natively by quite a bit. For my current address correction review, I needed to first sign up to a reference data provider on the Azure Data Market site. For this example, I used Melissa Data’s Address Check Service. They offer free one-month trials, so if you wish to follow along, or need to add address quality to your own data, I encourage you to sign up with them. Once I subscribed to the desired Reference Data Provider, I navigated my browser to the Account Keys within My Account to view the generated account key, which I then inserted into the DQS Client – Configuration under the Administration area. Step by Step to Guide That was all it took to hook in the subscribed provider -Melissa Data- directly to my DQS Client. The next step was for me to attach and map in my Reference Data from the newly acquired reference data provider, to a domain in my knowledge base. On the DQS Client home screen, I selected “New Knowledge Base” under Knowledge Base Management on the left-hand side of the home screen. Under New Knowledge Base, I typed a Name and description of my new knowledge base, then proceeded to the Domain Management screen. Here I established a series of domains (fields) and then linked them all together as a composite domain (record set). Using the Create Domain button, I created the following domains according to the fields in my incoming data: Name Address Suite City State Zip I added a Suite column in my domain because Melissa Data has the ability to return missing Suites based on last name or company. And that’s a great benefit of using these third party providers, as they have data that the data steward would not normally have access to. The bottom line is, with these third party data providers, I can actually improve my data. Next, I created a composite domain (fulladdress) and added the (field) domains into the composite domain. This essentially groups our address fields together in a record to facilitate the full address cleansing they perform. I then selected my newly created composite domain and under the Reference Data tab, added my third party reference data provider –Melissa Data’s Address Check- and mapped in each domain that I had to the provider’s Schema. Now that my composite domain has been married to the Reference Data service, I can take the newly published knowledge base and create a project to cleanse and enrich my data. My next task was to create a new Data Quality project, mapping in my data source and matching it to the appropriate domain column, and then kick off the verification process. It took just a few minutes with some progress indicators indicating that it was working. When the process concluded, there was a helpful set of tabs that place the response records into categories: suggested; new; invalid; corrected (automatically); and correct. Accepting the suggestions provided by  Melissa Data allowed me to clean up all the records and flag the invalid ones. It is very apparent that DQS makes address data quality simplistic for any IT professional. Final Note As I have shown, DQS makes data quality very easy. Within minutes I was able to set up a data cleansing and enrichment routine within my data quality project, and ensure that my address data was clean, verified, and standardized against real reference data. As reviewed here, it’s easy to see how both SQL Server 2012 and DQS work to take what used to require a highly skilled developer, and empower an average business or database person to consume external services and clean data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL Utility, T SQL, Technology Tagged: DQS

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• Big Data – Is Big Data Relevant to me? – Big Data Questionnaires – Guest Post by Vinod Kumar

  - by Pinal Dave

  This guest post is by Vinod Kumar. Vinod Kumar has worked with SQL Server extensively since joining the industry over a decade ago. Working on various versions of SQL Server 7.0, Oracle 7.3 and other database technologies – he now works with the Microsoft Technology Center (MTC) as a Technology Architect. Let us read the blog post in Vinod’s own voice. I think the series from Pinal is a good one for anyone planning to start on Big Data journey from the basics. In my daily customer interactions this buzz of “Big Data” always comes up, I react generally saying – “Sir, do you really have a ‘Big Data’ problem or do you have a big Data problem?” Generally, there is a silence in the air when I ask this question. Data is everywhere in organizations – be it big data, small data, all data and for few it is bad data which is same as no data :). Wow, don’t discount me as someone who opposes “Big Data”, I am a big supporter as much as I am a critic of the abuse of this term by the people. In this post, I wanted to let my mind flow so that you can also think in the direction I want you to see these concepts. In any case, this is not an exhaustive dump of what is in my mind – but you will surely get the drift how I am going to question Big Data terms from customers!!! Is Big Data Relevant to me? Many of my customers talk to me like blank whiteboard with no idea – “why Big Data”. They want to jump into the bandwagon of technology and they want to decipher insights from their unexplored data a.k.a. unstructured data with structured data. So what are these industry scenario’s that come to mind? Here are some of them: Financials Fraud detection: Banks and Credit cards are monitoring your spending habits on real-time basis. Customer Segmentation: applies in every industry from Banking to Retail to Aviation to Utility and others where they deal with end customer who consume their products and services. Customer Sentiment Analysis: Responding to negative brand perception on social or amplify the positive perception. Sales and Marketing Campaign: Understand the impact and get closer to customer delight. Call Center Analysis: attempt to take unstructured voice recordings and analyze them for content and sentiment. Medical Reduce Re-admissions: How to build a proactive follow-up engagements with patients. Patient Monitoring: How to track Inpatient, Out-Patient, Emergency Visits, Intensive Care Units etc. Preventive Care: Disease identification and Risk stratification is a very crucial business function for medical. Claims fraud detection: There is no precise dollars that one can put here, but this is a big thing for the medical field. Retail Customer Sentiment Analysis, Customer Care Centers, Campaign Management. Supply Chain Analysis: Every sensors and RFID data can be tracked for warehouse space optimization. Location based marketing: Based on where a check-in happens retail stores can be optimize their marketing. Telecom Price optimization and Plans, Finding Customer churn, Customer loyalty programs Call Detail Record (CDR) Analysis, Network optimizations, User Location analysis Customer Behavior Analysis Insurance Fraud Detection & Analysis, Pricing based on customer Sentiment Analysis, Loyalty Management Agents Analysis, Customer Value Management This list can go on to other areas like Utility, Manufacturing, Travel, ITES etc. So as you can see, there are obviously interesting use cases for each of these industry verticals. These are just representative list. Where to start? A lot of times I try to quiz customers on a number of dimensions before starting a Big Data conversation. Are you getting the data you need the way you want it and in a timely manner? Can you get in and analyze the data you need? How quickly is IT to respond to your BI Requests? How easily can you get at the data that you need to run your business/department/project? How are you currently measuring your business? Can you get the data you need to react WITHIN THE QUARTER to impact behaviors to meet your numbers or is it always “rear-view mirror?” How are you measuring: The Brand Customer Sentiment Your Competition Your Pricing Your performance Supply Chain Efficiencies Predictive product / service positioning What are your key challenges of driving collaboration across your global business?  What the challenges in innovation? What challenges are you facing in getting more information out of your data? Note: Garbage-in is Garbage-out. Hold good for all reporting / analytics requirements Big Data POCs? A number of customers get into the realm of setting a small team to work on Big Data – well it is a great start from an understanding point of view, but I tend to ask a number of other questions to such customers. Some of these common questions are: To what degree is your advanced analytics (natural language processing, sentiment analysis, predictive analytics and classification) paired with your Big Data’s efforts? Do you have dedicated resources exploring the possibilities of advanced analytics in Big Data for your business line? Do you plan to employ machine learning technology while doing Advanced Analytics? How is Social Media being monitored in your organization? What is your ability to scale in terms of storage and processing power? Do you have a system in place to sort incoming data in near real time by potential value, data quality, and use frequency? Do you use event-driven architecture to manage incoming data? Do you have specialized data services that can accommodate different formats, security, and the management requirements of multiple data sources? Is your organization currently using or considering in-memory analytics? To what degree are you able to correlate data from your Big Data infrastructure with that from your enterprise data warehouse? Have you extended the role of Data Stewards to include ownership of big data components? Do you prioritize data quality based on the source system (that is Facebook/Twitter data has lower quality thresholds than radio frequency identification (RFID) for a tracking system)? Do your retention policies consider the different legal responsibilities for storing Big Data for a specific amount of time? Do Data Scientists work in close collaboration with Data Stewards to ensure data quality? How is access to attributes of Big Data being given out in the organization? Are roles related to Big Data (Advanced Analyst, Data Scientist) clearly defined? How involved is risk management in the Big Data governance process? Is there a set of documented policies regarding Big Data governance? Is there an enforcement mechanism or approach to ensure that policies are followed? Who is the key sponsor for your Big Data governance program? (The CIO is best) Do you have defined policies surrounding the use of social media data for potential employees and customers, as well as the use of customer Geo-location data? How accessible are complex analytic routines to your user base? What is the level of involvement with outside vendors and third parties in regard to the planning and execution of Big Data projects? What programming technologies are utilized by your data warehouse/BI staff when working with Big Data? These are some of the important questions I ask each customer who is actively evaluating Big Data trends for their organizations. These questions give you a sense of direction where to start, what to use, how to secure, how to analyze and more. Sign off Any Big data is analysis is incomplete without a compelling story. The best way to understand this is to watch Hans Rosling – Gapminder (2:17 to 6:06) videos about the third world myths. Don’t get overwhelmed with the Big Data buzz word, the destination to what your data speaks is important. In this blog post, we did not particularly look at any Big Data technologies. This is a set of questionnaire one needs to keep in mind as they embark their journey of Big Data. I did write some of the basics in my blog: Big Data – Big Hype yet Big Opportunity. Do let me know if these questions make sense?  Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• Big Data – Basics of Big Data Architecture – Day 4 of 21

  - by Pinal Dave

  In yesterday’s blog post we understood how Big Data evolution happened. Today we will understand basics of the Big Data Architecture. Big Data Cycle Just like every other database related applications, bit data project have its development cycle. Though three Vs (link) for sure plays an important role in deciding the architecture of the Big Data projects. Just like every other project Big Data project also goes to similar phases of the data capturing, transforming, integrating, analyzing and building actionable reporting on the top of  the data. While the process looks almost same but due to the nature of the data the architecture is often totally different. Here are few of the question which everyone should ask before going ahead with Big Data architecture. Questions to Ask How big is your total database? What is your requirement of the reporting in terms of time – real time, semi real time or at frequent interval? How important is the data availability and what is the plan for disaster recovery? What are the plans for network and physical security of the data? What platform will be the driving force behind data and what are different service level agreements for the infrastructure? This are just basic questions but based on your application and business need you should come up with the custom list of the question to ask. As I mentioned earlier this question may look quite simple but the answer will not be simple. When we are talking about Big Data implementation there are many other important aspects which we have to consider when we decide to go for the architecture. Building Blocks of Big Data Architecture It is absolutely impossible to discuss and nail down the most optimal architecture for any Big Data Solution in a single blog post, however, we can discuss the basic building blocks of big data architecture. Here is the image which I have built to explain how the building blocks of the Big Data architecture works. Above image gives good overview of how in Big Data Architecture various components are associated with each other. In Big Data various different data sources are part of the architecture hence extract, transform and integration are one of the most essential layers of the architecture. Most of the data is stored in relational as well as non relational data marts and data warehousing solutions. As per the business need various data are processed as well converted to proper reports and visualizations for end users. Just like software the hardware is almost the most important part of the Big Data Architecture. In the big data architecture hardware infrastructure is extremely important and failure over instances as well as redundant physical infrastructure is usually implemented. NoSQL in Data Management NoSQL is a very famous buzz word and it really means Not Relational SQL or Not Only SQL. This is because in Big Data Architecture the data is in any format. It can be unstructured, relational or in any other format or from any other data source. To bring all the data together relational technology is not enough, hence new tools, architecture and other algorithms are invented which takes care of all the kind of data. This is collectively called NoSQL. Tomorrow Next four days we will answer the Buzz Words – Hadoop. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• Big Data – Evolution of Big Data – Day 3 of 21

  - by Pinal Dave

  In yesterday’s blog post we answered what is the Big Data. Today we will understand why and how the evolution of Big Data has happened. Though the answer is very simple, I would like to tell it in the form of a history lesson. Data in Flat File In earlier days data was stored in the flat file and there was no structure in the flat file.  If any data has to be retrieved from the flat file it was a project by itself. There was no possibility of retrieving the data efficiently and data integrity has been just a term discussed without any modeling or structure around. Database residing in the flat file had more issues than we would like to discuss in today’s world. It was more like a nightmare when there was any data processing involved in the application. Though, applications developed at that time were also not that advanced the need of the data was always there and there was always need of proper data management. Edgar F Codd and 12 Rules Edgar Frank Codd was a British computer scientist who, while working for IBM, invented the relational model for database management, the theoretical basis for relational databases. He presented 12 rules for the Relational Database and suddenly the chaotic world of the database seems to see discipline in the rules. Relational Database was a promising land for all the unstructured database users. Relational Database brought into the relationship between data as well improved the performance of the data retrieval. Database world had immediately seen a major transformation and every single vendors and database users suddenly started to adopt the relational database models. Relational Database Management Systems Since Edgar F Codd proposed 12 rules for the RBDMS there were many different vendors who started them to build applications and tools to support the relationship between database. This was indeed a learning curve for many of the developer who had never worked before with the modeling of the database. However, as time passed by pretty much everybody accepted the relationship of the database and started to evolve product which performs its best with the boundaries of the RDBMS concepts. This was the best era for the databases and it gave the world extreme experts as well as some of the best products. The Entity Relationship model was also evolved at the same time. In software engineering, an Entity–relationship model (ER model) is a data model for describing a database in an abstract way. Enormous Data Growth Well, everything was going fine with the RDBMS in the database world. As there were no major challenges the adoption of the RDBMS applications and tools was pretty much universal. There was a race at times to make the developer’s life much easier with the RDBMS management tools. Due to the extreme popularity and easy to use system pretty much every data was stored in the RDBMS system. New age applications were built and social media took the world by the storm. Every organizations was feeling pressure to provide the best experience for their users based the data they had with them. While this was all going on at the same time data was growing pretty much every organization and application. Data Warehousing The enormous data growth now presented a big challenge for the organizations who wanted to build intelligent systems based on the data and provide near real time superior user experience to their customers. Various organizations immediately start building data warehousing solutions where the data was stored and processed. The trend of the business intelligence becomes the need of everyday. Data was received from the transaction system and overnight was processed to build intelligent reports from it. Though this is a great solution it has its own set of challenges. The relational database model and data warehousing concepts are all built with keeping traditional relational database modeling in the mind and it still has many challenges when unstructured data was present. Interesting Challenge Every organization had expertise to manage structured data but the world had already changed to unstructured data. There was intelligence in the videos, photos, SMS, text, social media messages and various other data sources. All of these needed to now bring to a single platform and build a uniform system which does what businesses need. The way we do business has also been changed. There was a time when user only got the features what technology supported, however, now users ask for the feature and technology is built to support the same. The need of the real time intelligence from the fast paced data flow is now becoming a necessity. Large amount (Volume) of difference (Variety) of high speed data (Velocity) is the properties of the data. The traditional database system has limits to resolve the challenges this new kind of the data presents. Hence the need of the Big Data Science. We need innovation in how we handle and manage data. We need creative ways to capture data and present to users. Big Data is Reality! Tomorrow In tomorrow’s blog post we will try to answer discuss Basics of Big Data Architecture. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• Working with Temporal Data in SQL Server

  - by Dejan Sarka

  My third Pluralsight course, Working with Temporal Data in SQL Server, is published. I am really proud on the second part of the course, where I discuss optimization of temporal queries. This was a nearly impossible task for decades. First solutions appeared only lately. I present all together six solutions (and one more that is not a solution), and I invented four of them. http://pluralsight.com/training/Courses/TableOfContents/working-with-temporal-data-sql-server

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• New Communications Industry Data Model with "Factory Installed" Predictive Analytics using Oracle Da

  - by charlie.berger

  Oracle Introduces Oracle Communications Data Model to Provide Actionable Insight for Communications Service Providers   We've integrated pre-installed analytical methodologies with the new Oracle Communications Data Model to deliver automated, simple, yet powerful predictive analytics solutions for customers.  Churn, sentiment analysis, identifying customer segments - all things that can be anticipated and hence, preconcieved and implemented inside an applications.  Read on for more information! TM Forum Management World, Nice, France - 18 May 2010 News Facts To help communications service providers (CSPs) manage and analyze rapidly growing data volumes cost effectively, Oracle today introduced the Oracle Communications Data Model. With the Oracle Communications Data Model, CSPs can achieve rapid time to value by quickly implementing a standards-based enterprise data warehouse that features communications industry-specific reporting, analytics and data mining. The combination of the Oracle Communications Data Model, Oracle Exadata and the Oracle Business Intelligence (BI) Foundation represents the most comprehensive data warehouse and BI solution for the communications industry. Also announced today, Hong Kong Broadband Network enhanced their data warehouse system, going live on Oracle Communications Data Model in three months. The leading provider increased its subscriber base by 37 percent in six months and reduced customer churn to less than one percent. Product Details Oracle Communications Data Model provides industry-specific schema and embedded analytics that address key areas such as customer management, marketing segmentation, product development and network health. CSPs can efficiently capture and monitor critical data and transform it into actionable information to support development and delivery of next-generation services using: More than 1,300 industry-specific measurements and key performance indicators (KPIs) such as network reliability statistics, provisioning metrics and customer churn propensity. Embedded OLAP cubes for extremely fast dimensional analysis of business information. Embedded data mining models for sophisticated trending and predictive analysis. Support for multiple lines of business, such as cable, mobile, wireline and Internet, which can be easily extended to support future requirements. With Oracle Communications Data Model, CSPs can jump start the implementation of a communications data warehouse in line with communications-industry standards including the TM Forum Information Framework (SID), formerly known as the Shared Information Model. Oracle Communications Data Model is optimized for any Oracle Database 11g platform, including Oracle Exadata, which can improve call data record query performance by 10x or more. Supporting Quotes "Oracle Communications Data Model covers a wide range of business areas that are relevant to modern communications service providers and is a comprehensive solution - with its data model and pre-packaged templates including BI dashboards, KPIs, OLAP cubes and mining models. It helps us save a great deal of time in building and implementing a customized data warehouse and enables us to leverage the advanced analytics quickly and more effectively," said Yasuki Hayashi, executive manager, NTT Comware Corporation. "Data volumes will only continue to grow as communications service providers expand next-generation networks, deploy new services and adopt new business models. They will increasingly need efficient, reliable data warehouses to capture key insights on data such as customer value, network value and churn probability. With the Oracle Communications Data Model, Oracle has demonstrated its commitment to meeting these needs by delivering data warehouse tools designed to fill communications industry-specific needs," said Elisabeth Rainge, program director, Network Software, IDC. "The TM Forum Conformance Mark provides reassurance to customers seeking standards-based, and therefore, cost-effective and flexible solutions. TM Forum is extremely pleased to work with Oracle to certify its Oracle Communications Data Model solution. Upon successful completion, this certification will represent the broadest and most complete implementation of the TM Forum Information Framework to date, with more than 130 aggregate business entities," said Keith Willetts, chairman and chief executive officer, TM Forum. Supporting Resources Oracle Communications Oracle Communications Data Model Data Sheet Oracle Communications Data Model Podcast Oracle Data Warehousing Oracle Communications on YouTube Oracle Communications on Delicious Oracle Communications on Facebook Oracle Communications on Twitter Oracle Communications on LinkedIn Oracle Database on Twitter The Data Warehouse Insider Blog

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• Big Data – How to become a Data Scientist and Learn Data Science? – Day 19 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned the importance of the analytics in Big Data Story. In this article we will understand how to become a Data Scientist for Big Data Story. Data Scientist is a new buzz word, everyone seems to be wanting to become Data Scientist. Let us go over a few key topics related to Data Scientist in this blog post. First of all we will understand what is a Data Scientist. In the new world of Big Data, I see pretty much everyone wants to become Data Scientist and there are lots of people I have already met who claims that they are Data Scientist. When I ask what is their role, I have got a wide variety of answers. What is Data Scientist? Data scientists are the experts who understand various aspects of the business and know how to strategies data to achieve the business goals. They should have a solid foundation of various data algorithms, modeling and statistics methodology. What do Data Scientists do? Data scientists understand the data very well. They just go beyond the regular data algorithms and builds interesting trends from available data. They innovate and resurrect the entire new meaning from the existing data. They are artists in disguise of computer analyst. They look at the data traditionally as well as explore various new ways to look at the data. Data Scientists do not wait to build their solutions from existing data. They think creatively, they think before the data has entered into the system. Data Scientists are visionary experts who understands the business needs and plan ahead of the time, this tremendously help to build solutions at rapid speed. Besides being data expert, the major quality of Data Scientists is “curiosity”. They always wonder about what more they can get from their existing data and how to get maximum out of future incoming data. Data Scientists do wonders with the data, which goes beyond the job descriptions of Data Analysist or Business Analysist. Skills Required for Data Scientists Here are few of the skills a Data Scientist must have. Expert level skills with statistical tools like SAS, Excel, R etc. Understanding Mathematical Models Hands-on with Visualization Tools like Tableau, PowerPivots, D3. j’s etc. Analytical skills to understand business needs Communication skills On the technology front any Data Scientists should know underlying technologies like (Hadoop, Cloudera) as well as their entire ecosystem (programming language, analysis and visualization tools etc.) . Remember that for becoming a successful Data Scientist one require have par excellent skills, just having a degree in a relevant education field will not suffice. Final Note Data Scientists is indeed very exciting job profile. As per research there are not enough Data Scientists in the world to handle the current data explosion. In near future Data is going to expand exponentially, and the need of the Data Scientists will increase along with it. It is indeed the job one should focus if you like data and science of statistics. Courtesy: emc Tomorrow In tomorrow’s blog post we will discuss about various Big Data Learning resources. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• Unstructured Data - The future of Data Administration

  Some have claimed that there is a problem with the way data is currently managed using the relational paradigm do to the rise of unstructured data in modern business. PCMag.com defines unstructured data as data that does not reside in a fixed location. They further explain that unstructured data refers to data in a free text form that is not bound to any specific structure. With the rise of unstructured data in the form of emails, spread sheets, images and documents the critics have a right to argue that the relational paradigm is not as effective as the object oriented data paradigm in managing this type of data. The relational paradigm relies heavily on structure and relationships in and between items of data. This type of paradigm works best in a relation database management system like Microsoft SQL, MySQL, and Oracle because data is forced to conform to a structure in the form of tables and relations can be derived from the existence of one or more tables. These critics also claim that database administrators have not kept up with reality because their primary focus in regards to data administration deals with structured data and the relational paradigm. The relational paradigm was developed in the 1970’s as a way to improve data management when compared to standard flat files. Little has changed since then, and modern database administrators need to know more than just how to handle structured data. That is why critics claim that today’s data professionals do not have the proper skills in order to store and maintain data for modern systems when compared to the skills of system designers, programmers , software engineers, and data designers  due to the industry trend of object oriented design and development. I think that they are wrong. I do not disagree that the industry is moving toward an object oriented approach to development with the potential to use more of an object oriented approach to data.   However, I think that it is business itself that is limiting database administrators from changing how data is stored because of the potential costs, and impact that might occur by altering any part of stored data. Furthermore, database administrators like all technology workers constantly are trying to improve their technical skills in order to excel in their job, so I think that accusing data professional is not just when the root cause of the lack of innovation is controlled by business, and it is business that will suffer for their inability to keep up with technology. One way for database professionals to better prepare for the future of database management is start working with data in the form of objects and so that they can extract data from the objects so that the stored information within objects can be used in relation to the data stored in a using the relational paradigm. Furthermore, I think the use of pattern matching will increase with the increased use of unstructured data because object can be selected, filtered and altered based on the existence of a pattern found within an object.

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• Databases in Source Control

  - by Grant Fritchey

  I’ve been working as a database professional for quite a long time. But originally, I was a developer. And I loved being a developer. There was this constant feedback loop of a job well done, your code compiled and it ran. Every time this happened successfully, you’d check it into source control. These days you have to add another step; the code passed all the tests, unit, line, regression, qa, whatever, then into source control it goes. As a matter of fact, when I first made the jump from developer to DBA/database developer/database professional, source control was the one thing I couldn’t believe was missing from the DBA toolbox. Come to find out, source control was only the beginning of what was missing from your standard DBAs set of skills. Don’t get me wrong. I’m not disrespecting the DBA. They’re focused where they should be, on your production data. But there has to be a method for developing applications that include databases and the database side of that development and deployment process has long been lacking. This lack of development and deployment methodologies is a part of what has given rise to some of the wackier implementations of Object Relational Mapping tools, the NoSQL movement, and some of the other foul cursing that is directed towards databases, DBAs, and database development by application developers. Some of that is well earned. A lot isn’t. But it is a fact that database professionals, in general, do not have as sophisticated a model for managing development and deployment as application developers do. We could charge out and start trying to come up with our own standards and methods. I’m sure people have done exactly that. However, I’m lazy, and not terribly bright. Rather than try to invent a whole new process, I’m going to look to my developer roots and choose instead to emulate the developers. They’re sitting over there across the hall from me working with SCRUM/Agile/Waterfall/Object Driven/Feature Driven/Test Driven development processes that they’ve been polishing for years. What if I just started working on database development the same way they work on code development? Win! Ah, but now I have to have a mechanism for treating my database like application code. First, I need a method for getting it into source control. That’s where Red Gate’s SQL Source Control comes into the picture. SQL Source Control works within SQL Server Management Studio to connect your database objects up to the source control system of your choice. Right out of the box SQL Source Control can link to TFS, SVN or Vault. With a little work you can connect it to Git or just about any other source control system. With the ability to get my database into source control, a lot of possibilities for more direct integration with the application development teams open up.

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• Developing an analytics's system processing large amounts of data - where to start

  - by Ryan

  Imagine you're writing some sort of Web Analytics system - you're recording raw page hits along with some extra things like tagging cookies etc and then producing stats such as Which pages got most traffic over a time period Which referers sent most traffic Goals completed (goal being a view of a particular page) And more advanced things like which referers sent the most number of vistors who later hit a goal. The naieve way of approaching this would be to throw it in a relational database and run queries over it - but that won't scale. You could pre-calculate everything (have a queue of incoming 'hits' and use to update report tables) - but what if you later change a goal - how could you efficiently re-calculate just the data that would be effected. Obviously this has been done before ;) so any tips on where to start, methods & examples, architecture, technologies etc.

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• Big Data – Beginning Big Data – Day 1 of 21

  - by Pinal Dave

  What is Big Data? I want to learn Big Data. I have no clue where and how to start learning about it. Does Big Data really means data is big? What are the tools and software I need to know to learn Big Data? I often receive questions which I mentioned above. They are good questions and honestly when we search online, it is hard to find authoritative and authentic answers. I have been working with Big Data and NoSQL for a while and I have decided that I will attempt to discuss this subject over here in the blog. In the next 21 days we will understand what is so big about Big Data. Big Data – Big Thing! Big Data is becoming one of the most talked about technology trends nowadays. The real challenge with the big organization is to get maximum out of the data already available and predict what kind of data to collect in the future. How to take the existing data and make it meaningful that it provides us accurate insight in the past data is one of the key discussion points in many of the executive meetings in organizations. With the explosion of the data the challenge has gone to the next level and now a Big Data is becoming the reality in many organizations. Big Data – A Rubik’s Cube I like to compare big data with the Rubik’s cube. I believe they have many similarities. Just like a Rubik’s cube it has many different solutions. Let us visualize a Rubik’s cube solving challenge where there are many experts participating. If you take five Rubik’s cube and mix up the same way and give it to five different expert to solve it. It is quite possible that all the five people will solve the Rubik’s cube in fractions of the seconds but if you pay attention to the same closely, you will notice that even though the final outcome is the same, the route taken to solve the Rubik’s cube is not the same. Every expert will start at a different place and will try to resolve it with different methods. Some will solve one color first and others will solve another color first. Even though they follow the same kind of algorithm to solve the puzzle they will start and end at a different place and their moves will be different at many occasions. It is  nearly impossible to have a exact same route taken by two experts. Big Market and Multiple Solutions Big Data is exactly like a Rubik’s cube – even though the goal of every organization and expert is same to get maximum out of the data, the route and the starting point are different for each organization and expert. As organizations are evaluating and architecting big data solutions they are also learning the ways and opportunities which are related to Big Data. There is not a single solution to big data as well there is not a single vendor which can claim to know all about Big Data. Honestly, Big Data is too big a concept and there are many players – different architectures, different vendors and different technology. What is Next? In this 31 days series we will be exploring many essential topics related to big data. I do not claim that you will be master of the subject after 31 days but I claim that I will be covering following topics in easy to understand language. Architecture of Big Data Big Data a Management and Implementation Different Technologies – Hadoop, Mapreduce Real World Conversations Best Practices Tomorrow In tomorrow’s blog post we will try to answer one of the very essential questions – What is Big Data? Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• Reading data from an Entity Framework data model through a WCF Data Service

  - by nikolaosk

  This is going to be the fourth post of a series of posts regarding ASP.Net and the Entity Framework and how we can use Entity Framework to access our datastore. You can find the first one here , the second one here and the third one here . I have a post regarding ASP.Net and EntityDataSource. You can read it here .I have 3 more posts on Profiling Entity Framework applications. You can have a look at them here , here and here . Microsoft with .Net 3.0 Framework, introduced WCF. WCF is Microsoft's...(read more)

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• Big Data’s Killer App…

  - by jean-pierre.dijcks

  Recently Keith spent  some time talking about the cloud on this blog and I will spare you my thoughts on the whole thing. What I do want to write down is something about the Big Data movement and what I think is the killer app for Big Data... Where is this coming from, ok, I confess... I spent 3 days in cloud land at the Cloud Connect conference in Santa Clara and it was quite a lot of fun. One of the nice things at Cloud Connect was that there was a track dedicated to Big Data, which prompted me to some extend to write this post. What is Big Data anyways? The most valuable point made in the Big Data track was that Big Data in itself is not very cool. Doing something with Big Data is what makes all of this cool and interesting to a business user! The other good insight I got was that a lot of people think Big Data means a single gigantic monolithic system holding gazillions of bytes or documents or log files. Well turns out that most people in the Big Data track are talking about a lot of collections of smaller data sets. So rather than thinking "big = monolithic" you should be thinking "big = many data sets". This is more than just theoretical, it is actually relevant when thinking about big data and how to process it. It is important because it means that the platform that stores data will most likely consist out of multiple solutions. You may be storing logs on something like HDFS, you may store your customer information in Oracle and you may store distilled clickstream information in some distilled form in MySQL. The big question you will need to solve is not what lives where, but how to get it all together and get some value out of all that data. NoSQL and MapReduce Nope, sorry, this is not the killer app... and no I'm not saying this because my business card says Oracle and I'm therefore biased. I think language is important, but as with storage I think pragmatic is better. In other words, some questions can be answered with SQL very efficiently, others can be answered with PERL or TCL others with MR. History should teach us that anyone trying to solve a problem will use any and all tools around. For example, most data warehouses (Big Data 1.0?) get a lot of data in flat files. Everyone then runs a bunch of shell scripts to massage or verify those files and then shoves those files into the database. We've even built shell script support into external tables to allow for this. I think the Big Data projects will do the same. Some people will use MapReduce, although I would argue that things like Cascading are more interesting, some people will use Java. Some data is stored on HDFS making Cascading the way to go, some data is stored in Oracle and SQL does do a good job there. As with storage and with history, be pragmatic and use what fits and neither NoSQL nor MR will be the one and only. Also, a language, while important, does in itself not deliver business value. So while cool it is not a killer app... Vertical Behavioral Analytics This is the killer app! And you are now thinking: "what does that mean?" Let's decompose that heading. First of all, analytics. I would think you had guessed by now that this is really what I'm after, and of course you are right. But not just analytics, which has a very large scope and means many things to many people. I'm not just after Business Intelligence (analytics 1.0?) or data mining (analytics 2.0?) but I'm after something more interesting that you can only do after collecting large volumes of specific data. That all important data is about behavior. What do my customers do? More importantly why do they behave like that? If you can figure that out, you can tailor web sites, stores, products etc. to that behavior and figure out how to be successful. Today's behavior that is somewhat easily tracked is web site clicks, search patterns and all of those things that a web site or web server tracks. that is where the Big Data lives and where these patters are now emerging. Other examples however are emerging, and one of the examples used at the conference was about prediction churn for a telco based on the social network its members are a part of. That social network is not about LinkedIn or Facebook, but about who calls whom. I call you a lot, you switch provider, and I might/will switch too. And that just naturally brings me to the next word, vertical. Vertical in this context means per industry, e.g. communications or retail or government or any other vertical. The reason for being more specific than just behavioral analytics is that each industry has its own data sources, has its own quirky logic and has its own demands and priorities. Of course, the methods and some of the software will be common and some will have both retail and service industry analytics in place (your corner coffee store for example). But the gist of it all is that analytics that can predict customer behavior for a specific focused group of people in a specific industry is what makes Big Data interesting. Building a Vertical Behavioral Analysis System Well, that is going to be interesting. I have not seen much going on in that space and if I had to have some criticism on the cloud connect conference it would be the lack of concrete user cases on big data. The telco example, while a step into the vertical behavioral part is not really on big data. It used a sample of data from the customers' data warehouse. One thing I do think, and this is where I think parts of the NoSQL stuff come from, is that we will be doing this analysis where the data is. Over the past 10 years we at Oracle have called this in-database analytics. I guess we were (too) early? Now the entire market is going there including companies like SAS. In-place btw does not mean "no data movement at all", what it means that you will do this on data's permanent home. For SAS that is kind of the current problem. Most of the inputs live in a data warehouse. So why move it into SAS and back? That all worked with 1 TB data warehouses, but when we are looking at 100TB to 500 TB of distilled data... Comments? As it is still early days with these systems, I'm very interested in seeing reactions and thoughts to some of these thoughts...

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• Accelerate your SOA with Data Integration - Live Webinar Tuesday!

  - by dain.hansen

  Need to put wind in your SOA sails? Organizations are turning more and more to Real-time data integration to complement their Service Oriented Architecture. The benefit? Lowering costs through consolidating legacy systems, reducing risk of bad data polluting their applications, and shortening the time to deliver new service offerings. Join us on Tuesday April 13th, 11AM PST for our live webinar on the value of combining SOA and Data Integration together. In this webcast you'll learn how to innovate across your applications swiftly and at a lower cost using Oracle Data Integration technologies: Oracle Data Integrator Enterprise Edition, Oracle GoldenGate, and Oracle Data Quality. You'll also hear: Best practices for building re-usable data services that are high performing and scalable across the enterprise How real-time data integration can maximize SOA returns while providing continuous availability for your mission critical applications Architectural approaches to speed service implementation and delivery times, with pre-integrations to CRM, ERP, BI, and other packaged applications Register now for this live webinar!

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• Big Data – Basics of Big Data Analytics – Day 18 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned the importance of the various components in Big Data Story. In this article we will understand what are the various analytics tasks we try to achieve with the Big Data and the list of the important tools in Big Data Story. When you have plenty of the data around you what is the first thing which comes to your mind? “What do all these data means?” Exactly – the same thought comes to my mind as well. I always wanted to know what all the data means and what meaningful information I can receive out of it. Most of the Big Data projects are built to retrieve various intelligence all this data contains within it. Let us take example of Facebook. When I look at my friends list of Facebook, I always want to ask many questions such as - On which date my maximum friends have a birthday? What is the most favorite film of my most of the friends so I can talk about it and engage them? What is the most liked placed to travel my friends? Which is the most disliked cousin for my friends in India and USA so when they travel, I do not take them there. There are many more questions I can think of. This illustrates that how important it is to have analysis of Big Data. Here are few of the kind of analysis listed which you can use with Big Data. Slicing and Dicing: This means breaking down your data into smaller set and understanding them one set at a time. This also helps to present various information in a variety of different user digestible ways. For example if you have data related to movies, you can use different slide and dice data in various formats like actors, movie length etc. Real Time Monitoring: This is very crucial in social media when there are any events happening and you wanted to measure the impact at the time when the event is happening. For example, if you are using twitter when there is a football match, you can watch what fans are talking about football match on twitter when the event is happening. Anomaly Predication and Modeling: If the business is running normal it is alright but if there are signs of trouble, everyone wants to know them early on the hand. Big Data analysis of various patterns can be very much helpful to predict future. Though it may not be always accurate but certain hints and signals can be very helpful. For example, lots of data can help conclude that if there is lots of rain it can increase the sell of umbrella. Text and Unstructured Data Analysis: unstructured data are now getting norm in the new world and they are a big part of the Big Data revolution. It is very important that we Extract, Transform and Load the unstructured data and make meaningful data out of it. For example, analysis of lots of images, one can predict that people like to use certain colors in certain months in their cloths. Big Data Analytics Solutions There are many different Big Data Analystics Solutions out in the market. It is impossible to list all of them so I will list a few of them over here. Tableau – This has to be one of the most popular visualization tools out in the big data market. SAS – A high performance analytics and infrastructure company IBM and Oracle – They have a range of tools for Big Data Analysis Tomorrow In tomorrow’s blog post we will discuss about very important components of the Big Data Ecosystem – Data Scientist. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• September 2011 Release of the Ajax Control Toolkit

  - by Stephen Walther

  I’m happy to announce the release of the September 2011 Ajax Control Toolkit. This release has several important new features including: Date ranges – When using the Calendar extender, you can specify a start and end date and a user can pick only those dates which fall within the specified range. This was the fourth top-voted feature request for the Ajax Control Toolkit at CodePlex. Twitter Control – You can use the new Twitter control to display recent tweets associated with a particular Twitter user or tweets which match a search query. Gravatar Control – You can use the new Gravatar control to display a unique image for each user of your website. Users can upload custom images to the Gravatar.com website or the Gravatar control can display a unique, auto-generated, image for a user. You can download this release this very minute by visiting CodePlex: http://AjaxControlToolkit.CodePlex.com Alternatively, you can execute the following command from the Visual Studio NuGet console: Improvements to the Ajax Control Toolkit Calendar Control The Ajax Control Toolkit Calendar extender control is one of the most heavily used controls from the Ajax Control Toolkit. The developers on the Superexpert team spent the last sprint focusing on improving this control. There are three important changes that we made to the Calendar control: we added support for date ranges, we added support for highlighting today’s date, and we made fixes to several bugs related to time zones and daylight savings. Using Calendar Date Ranges One of the top-voted feature requests for the Ajax Control Toolkit was a request to add support for date ranges to the Calendar control (this was the fourth most voted feature request at CodePlex). With the latest release of the Ajax Control Toolkit, the Calendar extender now supports date ranges. For example, the following page illustrates how you can create a popup calendar which allows a user only to pick dates between March 2, 2009 and May 16, 2009. <%@ Page Language="C#" AutoEventWireup="true" CodeBehind="CalendarDateRange.aspx.cs" Inherits="WebApplication1.CalendarDateRange" %> <%@ Register TagPrefix="asp" Namespace="AjaxControlToolkit" Assembly="AjaxControlToolkit" %> <html> <head runat="server"> <title>Calendar Date Range</title> </head> <body> <form id="form1" runat="server"> <asp:ToolkitScriptManager ID="tsm" runat="server" /> <asp:TextBox ID="txtHotelReservationDate" runat="server" /> <asp:CalendarExtender ID="Calendar1" TargetControlID="txtHotelReservationDate" StartDate="3/2/2009" EndDate="5/16/2009" SelectedDate="3/2/2009" runat="server" /> </form> </body> </html> This page contains three controls: an Ajax Control Toolkit ToolkitScriptManager control, a standard ASP.NET TextBox control, and an Ajax Control Toolkit CalendarExtender control. Notice that the Calendar control includes StartDate and EndDate properties which restrict the range of valid dates. The Calendar control shows days, months, and years outside of the valid range as struck out. You cannot select days, months, or years which fall outside of the range. The following video illustrates interacting with the new date range feature: If you want to experiment with a live version of the Ajax Control Toolkit Calendar extender control then you can visit the Calendar Sample Page at the Ajax Control Toolkit Sample Site. Highlighted Today’s Date Another highly requested feature for the Calendar control was support for highlighting today’s date. The Calendar control now highlights the user’s current date regardless of the user’s time zone. Fixes to Time Zone and Daylight Savings Time Bugs We fixed several significant Calendar extender bugs related to time zones and daylight savings time. For example, previously, when you set the Calendar control’s SelectedDate property to the value 1/1/2007 then the selected data would appear as 12/31/2006 or 1/1/2007 or 1/2/2007 depending on the server time zone. For example, if your server time zone was set to Samoa (UTC-11:00), then setting SelectedDate=”1/1/2007” would result in “12/31/2006” being selected in the Calendar. Users of the Calendar extender control found this behavior confusing. After careful consideration, we decided to change the Calendar extender so that it interprets all dates as UTC dates. In other words, if you set StartDate=”1/1/2007” then the Calendar extender parses the date as 1/1/2007 UTC instead of parsing the date according to the server time zone. By interpreting all dates as UTC dates, we avoid all of the reported issues with the SelectedDate property showing the wrong date. Furthermore, when you set the StartDate and EndDate properties, you know that the same StartDate and EndDate will be selected regardless of the time zone associated with the server or associated with the browser. The date 1/1/2007 will always be the date 1/1/2007. The New Twitter Control This release of the Ajax Control Toolkit introduces a new twitter control. You can use the Twitter control to display recent tweets associated with a particular twitter user. You also can use this control to show the results of a twitter search. The following page illustrates how you can use the Twitter control to display recent tweets made by Scott Hanselman: <%@ Page Language="C#" AutoEventWireup="true" CodeBehind="TwitterProfile.aspx.cs" Inherits="WebApplication1.TwitterProfile" %> <%@ Register TagPrefix="asp" Namespace="AjaxControlToolkit" Assembly="AjaxControlToolkit" %> <html > <head runat="server"> <title>Twitter Profile</title> </head> <body> <form id="form1" runat="server"> <asp:ToolkitScriptManager ID="tsm" runat="server" /> <asp:Twitter ID="Twitter1" ScreenName="shanselman" runat="server" /> </form> </body> </html> This page includes two Ajax Control Toolkit controls: the ToolkitScriptManager control and the Twitter control. The Twitter control is set to display tweets from Scott Hanselman (shanselman): You also can use the Twitter control to display the results of a search query. For example, the following page displays all recent tweets related to the Ajax Control Toolkit: Twitter limits the number of times that you can interact with their API in an hour. Twitter recommends that you cache results on the server (https://dev.twitter.com/docs/rate-limiting). By default, the Twitter control caches results on the server for a duration of 5 minutes. You can modify the cache duration by assigning a value (in seconds) to the Twitter control's CacheDuration property. The Twitter control wraps a standard ASP.NET ListView control. You can customize the appearance of the Twitter control by modifying its LayoutTemplate, StatusTemplate, AlternatingStatusTemplate, and EmptyDataTemplate. To learn more about the new Twitter control, visit the live Twitter Sample Page. The New Gravatar Control The September 2011 release of the Ajax Control Toolkit also includes a new Gravatar control. This control makes it easy to display a unique image for each user of your website. A Gravatar is associated with an email address. You can visit Gravatar.com and upload an image and associate the image with your email address. That way, every website which uses Gravatars (such as the www.ASP.NET website) will display your image next to your name. For example, I visited the Gravatar.com website and associated an image of a Koala Bear with the email address [email protected]. The following page illustrates how you can use the Gravatar control to display the Gravatar image associated with the [email protected] email address: <%@ Page Language="C#" AutoEventWireup="true" CodeBehind="GravatarDemo.aspx.cs" Inherits="WebApplication1.GravatarDemo" %> <%@ Register TagPrefix="asp" Namespace="AjaxControlToolkit" Assembly="AjaxControlToolkit" %> <html xmlns="http://www.w3.org/1999/xhtml"> <head id="Head1" runat="server"> <title>Gravatar Demo</title> </head> <body> <form id="form1" runat="server"> <asp:ToolkitScriptManager ID="tsm" runat="server" /> <asp:Gravatar ID="Gravatar1" Email="[email protected]" runat="server" /> </form> </body> </html> The page above simply displays the Gravatar image associated with the [email protected] email address: If a user has not uploaded an image to Gravatar.com then you can auto-generate a unique image for the user from the user email address. The Gravatar control supports four types of auto-generated images: Identicon -- A different geometric pattern is generated for each unrecognized email. MonsterId -- A different image of a monster is generated for each unrecognized email. Wavatar -- A different image of a face is generated for each unrecognized email. Retro -- A different 8-bit arcade-style face is generated for each unrecognized email. For example, there is no Gravatar image associated with the email address [email protected]. The following page displays an auto-generated MonsterId for this email address: <%@ Page Language="C#" AutoEventWireup="true" CodeBehind="GravatarMonster.aspx.cs" Inherits="WebApplication1.GravatarMonster" %> <%@ Register TagPrefix="asp" Namespace="AjaxControlToolkit" Assembly="AjaxControlToolkit" %> <html xmlns="http://www.w3.org/1999/xhtml"> <head id="Head1" runat="server"> <title>Gravatar Monster</title> </head> <body> <form id="form1" runat="server"> <asp:ToolkitScriptManager ID="tsm" runat="server" /> <asp:Gravatar ID="Gravatar1" Email="[email protected]" DefaultImageBehavior="MonsterId" runat="server" /> </form> </body> </html> The page above generates the following image automatically from the supplied email address: To learn more about the properties of the new Gravatar control, visit the live Gravatar Sample Page. ASP.NET Connections Talk on the Ajax Control Toolkit If you are interested in learning more about the changes that we are making to the Ajax Control Toolkit then please come to my talk on the Ajax Control Toolkit at the upcoming ASP.NET Connections conference. In the talk, I will present a summary of the changes that we have made to the Ajax Control Toolkit over the last several months and discuss our future plans. Do you have ideas for new Ajax Control Toolkit controls? Ideas for improving the toolkit? Come to my talk – I would love to hear from you. You can register for the ASP.NET Connections conference by visiting the following website: Register for ASP.NET Connections   Summary The previous release of the Ajax Control Toolkit – the July 2011 Release – has had over 100,000 downloads. That is a huge number of developers who are working with the Ajax Control Toolkit. We are really excited about the new features which we added to the Ajax Control Toolkit in the latest September sprint. We hope that you find the updated Calender control, the new Twitter control, and the new Gravatar control valuable when building your ASP.NET Web Forms applications.

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• July 2013 Release of the Ajax Control Toolkit

  - by Stephen.Walther

  I’m super excited to announce the July 2013 release of the Ajax Control Toolkit. You can download the new version of the Ajax Control Toolkit from CodePlex (http://ajaxControlToolkit.CodePlex.com) or install the Ajax Control Toolkit from NuGet: With this release, we have completely rewritten the way the Ajax Control Toolkit combines, minifies, gzips, and caches JavaScript files. The goal of this release was to improve the performance of the Ajax Control Toolkit and make it easier to create custom Ajax Control Toolkit controls. Improving Ajax Control Toolkit Performance Previous releases of the Ajax Control Toolkit optimized performance for a single page but not multiple pages. When you visited each page in an app, the Ajax Control Toolkit would combine all of the JavaScript files required by the controls in the page into a new JavaScript file. So, even if every page in your app used the exact same controls, visitors would need to download a new combined Ajax Control Toolkit JavaScript file for each page visited. Downloading new scripts for each page that you visit does not lead to good performance. In general, you want to make as few requests for JavaScript files as possible and take maximum advantage of caching. For most apps, you would get much better performance if you could specify all of the Ajax Control Toolkit controls that you need for your entire app and create a single JavaScript file which could be used across your entire app. What a great idea! Introducing Control Bundles With this release of the Ajax Control Toolkit, we introduce the concept of Control Bundles. You define a Control Bundle to indicate the set of Ajax Control Toolkit controls that you want to use in your app. You define Control Bundles in a file located in the root of your application named AjaxControlToolkit.config. For example, the following AjaxControlToolkit.config file defines two Control Bundles: <ajaxControlToolkit> <controlBundles> <controlBundle> <control name="CalendarExtender" /> <control name="ComboBox" /> </controlBundle> <controlBundle name="CalendarBundle"> <control name="CalendarExtender"></control> </controlBundle> </controlBundles> </ajaxControlToolkit> The first Control Bundle in the file above does not have a name. When a Control Bundle does not have a name then it becomes the default Control Bundle for your entire application. The default Control Bundle is used by the ToolkitScriptManager by default. For example, the default Control Bundle is used when you declare the ToolkitScriptManager like this:  <ajaxToolkit:ToolkitScriptManager runat=”server” /> The default Control Bundle defined in the file above includes all of the scripts required for the CalendarExtender and ComboBox controls. All of the scripts required for both of these controls are combined, minified, gzipped, and cached automatically. The AjaxControlToolkit.config file above also defines a second Control Bundle with the name CalendarBundle. Here’s how you would use the CalendarBundle with the ToolkitScriptManager: <ajaxToolkit:ToolkitScriptManager runat="server"> <ControlBundles> <ajaxToolkit:ControlBundle Name="CalendarBundle" /> </ControlBundles> </ajaxToolkit:ToolkitScriptManager> In this case, only the JavaScript files required by the CalendarExtender control, and not the ComboBox, would be downloaded because the CalendarBundle lists only the CalendarExtender control. You can use multiple named control bundles with the ToolkitScriptManager and you will get all of the scripts from both bundles. Support for ControlBundles is a new feature of the ToolkitScriptManager that we introduced with this release. We extended the ToolkitScriptManager to support the Control Bundles that you can define in the AjaxControlToolkit.config file. Let me be explicit about the rules for Control Bundles: 1. If you do not create an AjaxControlToolkit.config file then the ToolkitScriptManager will download all of the JavaScript files required for all of the controls in the Ajax Control Toolkit. This is the easy but low performance option. 2. If you create an AjaxControlToolkit.config file and create a ControlBundle without a name then the ToolkitScriptManager uses that Control Bundle by default. For example, if you plan to use only the CalendarExtender and ComboBox controls in your application then you should create a default bundle that lists only these two controls. 3. If you create an AjaxControlToolkit.config file and create one or more named Control Bundles then you can use these named Control Bundles with the ToolkitScriptManager. For example, you might want to use different subsets of the Ajax Control Toolkit controls in different sections of your app. I should also mention that you can use the AjaxControlToolkit.config file with custom Ajax Control Toolkit controls – new controls that you write. For example, here is how you would register a set of custom controls from an assembly named MyAssembly: <ajaxControlToolkit> <controlBundles> <controlBundle name="CustomBundle"> <control name="MyAssembly.MyControl1" assembly="MyAssembly" /> <control name="MyAssembly.MyControl2" assembly="MyAssembly" /> </controlBundle> </ajaxControlToolkit> What about ASP.NET Bundling and Minification? The idea of Control Bundles is similar to the idea of Script Bundles used in ASP.NET Bundling and Minification. You might be wondering why we didn’t simply use Script Bundles with the Ajax Control Toolkit. There were several reasons. First, ASP.NET Bundling does not work with scripts embedded in an assembly. Because all of the scripts used by the Ajax Control Toolkit are embedded in the AjaxControlToolkit.dll assembly, ASP.NET Bundling was not an option. Second, Web Forms developers typically think at the level of controls and not at the level of individual scripts. We believe that it makes more sense for a Web Forms developer to specify the controls that they need in an app (CalendarExtender, ToggleButton) instead of the individual scripts that they need in an app (the 15 or so scripts required by the CalenderExtender). Finally, ASP.NET Bundling does not work with older versions of ASP.NET. The Ajax Control Toolkit needs to support ASP.NET 3.5, ASP.NET 4.0, and ASP.NET 4.5. Therefore, using ASP.NET Bundling was not an option. There is nothing wrong with using Control Bundles and Script Bundles side-by-side. The ASP.NET 4.0 and 4.5 ToolkitScriptManager supports both approaches to bundling scripts. Using the AjaxControlToolkit.CombineScriptsHandler Browsers cache JavaScript files by URL. For example, if you request the exact same JavaScript file from two different URLs then the exact same JavaScript file must be downloaded twice. However, if you request the same JavaScript file from the same URL more than once then it only needs to be downloaded once. With this release of the Ajax Control Toolkit, we have introduced a new HTTP Handler named the AjaxControlToolkit.CombineScriptsHandler. If you register this handler in your web.config file then the Ajax Control Toolkit can cache your JavaScript files for up to one year in the future automatically. You should register the handler in two places in your web.config file: in the <httpHandlers> section and the <system.webServer> section (don’t forget to register the handler for the AjaxFileUpload while you are there!). <httpHandlers> <add verb="*" path="AjaxFileUploadHandler.axd" type="AjaxControlToolkit.AjaxFileUploadHandler, AjaxControlToolkit" /> <add verb="*" path="CombineScriptsHandler.axd" type="AjaxControlToolkit.CombineScriptsHandler, AjaxControlToolkit" /> </httpHandlers> <system.webServer> <validation validateIntegratedModeConfiguration="false" /> <handlers> <add name="AjaxFileUploadHandler" verb="*" path="AjaxFileUploadHandler.axd" type="AjaxControlToolkit.AjaxFileUploadHandler, AjaxControlToolkit" /> <add name="CombineScriptsHandler" verb="*" path="CombineScriptsHandler.axd" type="AjaxControlToolkit.CombineScriptsHandler, AjaxControlToolkit" /> </handlers> <system.webServer> The handler is only used in release mode and not in debug mode. You can enable release mode in your web.config file like this: <compilation debug=”false”> You also can override the web.config setting with the ToolkitScriptManager like this: <act:ToolkitScriptManager ScriptMode=”Release” runat=”server”/> In release mode, scripts are combined, minified, gzipped, and cached with a far future cache header automatically. When the handler is not registered, scripts are requested from the page that contains the ToolkitScriptManager: When the handler is registered in the web.config file, scripts are requested from the handler: If you want the best performance, always register the handler. That way, the Ajax Control Toolkit can cache the bundled scripts across page requests with a far future cache header. If you don’t register the handler then a new JavaScript file must be downloaded whenever you travel to a new page. Dynamic Bundling and Minification Previous releases of the Ajax Control Toolkit used a Visual Studio build task to minify the JavaScript files used by the Ajax Control Toolkit controls. The disadvantage of this approach to minification is that it made it difficult to create custom Ajax Control Toolkit controls. Starting with this release of the Ajax Control Toolkit, we support dynamic minification. The JavaScript files in the Ajax Control Toolkit are minified at runtime instead of at build time. Scripts are minified only when in release mode. You can specify release mode with the web.config file or with the ToolkitScriptManager ScriptMode property. Because of this change, the Ajax Control Toolkit now depends on the Ajax Minifier. You must include a reference to AjaxMin.dll in your Visual Studio project or you cannot take advantage of runtime minification. If you install the Ajax Control Toolkit from NuGet then AjaxMin.dll is added to your project as a NuGet dependency automatically. If you download the Ajax Control Toolkit from CodePlex then the AjaxMin.dll is included in the download. This change means that you no longer need to do anything special to create a custom Ajax Control Toolkit. As an open source project, we hope more people will contribute to the Ajax Control Toolkit (Yes, I am looking at you.) We have been working hard on making it much easier to create new custom controls. More on this subject with the next release of the Ajax Control Toolkit. A Single Visual Studio Solution We also made substantial changes to the Visual Studio solution and projects used by the Ajax Control Toolkit with this release. This change will matter to you only if you need to work directly with the Ajax Control Toolkit source code. In previous releases of the Ajax Control Toolkit, we maintained separate solution and project files for ASP.NET 3.5, ASP.NET 4.0, and ASP.NET 4.5. Starting with this release, we now support a single Visual Studio 2012 solution that takes advantage of multi-targeting to build ASP.NET 3.5, ASP.NET 4.0, and ASP.NET 4.5 versions of the toolkit. This change means that you need Visual Studio 2012 to open the Ajax Control Toolkit project downloaded from CodePlex. For details on how we setup multi-targeting, please see Budi Adiono’s blog post: http://www.budiadiono.com/2013/07/25/visual-studio-2012-multi-targeting-framework-project/ Summary You can take advantage of this release of the Ajax Control Toolkit to significantly improve the performance of your website. You need to do two things: 1) You need to create an AjaxControlToolkit.config file which lists the controls used in your app and 2) You need to register the AjaxControlToolkit.CombineScriptsHandler in the web.config file. We made substantial changes to the Ajax Control Toolkit with this release. We think these changes will result in much better performance for multipage apps and make the process of building custom controls much easier. As always, we look forward to hearing your feedback.

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• Big Data – What is Big Data – 3 Vs of Big Data – Volume, Velocity and Variety – Day 2 of 21

  - by Pinal Dave

  Data is forever. Think about it – it is indeed true. Are you using any application as it is which was built 10 years ago? Are you using any piece of hardware which was built 10 years ago? The answer is most certainly No. However, if I ask you – are you using any data which were captured 50 years ago, the answer is most certainly Yes. For example, look at the history of our nation. I am from India and we have documented history which goes back as over 1000s of year. Well, just look at our birthday data – atleast we are using it till today. Data never gets old and it is going to stay there forever.  Application which interprets and analysis data got changed but the data remained in its purest format in most cases. As organizations have grown the data associated with them also grew exponentially and today there are lots of complexity to their data. Most of the big organizations have data in multiple applications and in different formats. The data is also spread out so much that it is hard to categorize with a single algorithm or logic. The mobile revolution which we are experimenting right now has completely changed how we capture the data and build intelligent systems.  Big organizations are indeed facing challenges to keep all the data on a platform which give them a  single consistent view of their data. This unique challenge to make sense of all the data coming in from different sources and deriving the useful actionable information out of is the revolution Big Data world is facing. Defining Big Data The 3Vs that define Big Data are Variety, Velocity and Volume. Volume We currently see the exponential growth in the data storage as the data is now more than text data. We can find data in the format of videos, musics and large images on our social media channels. It is very common to have Terabytes and Petabytes of the storage system for enterprises. As the database grows the applications and architecture built to support the data needs to be reevaluated quite often. Sometimes the same data is re-evaluated with multiple angles and even though the original data is the same the new found intelligence creates explosion of the data. The big volume indeed represents Big Data. Velocity The data growth and social media explosion have changed how we look at the data. There was a time when we used to believe that data of yesterday is recent. The matter of the fact newspapers is still following that logic. However, news channels and radios have changed how fast we receive the news. Today, people reply on social media to update them with the latest happening. On social media sometimes a few seconds old messages (a tweet, status updates etc.) is not something interests users. They often discard old messages and pay attention to recent updates. The data movement is now almost real time and the update window has reduced to fractions of the seconds. This high velocity data represent Big Data. Variety Data can be stored in multiple format. For example database, excel, csv, access or for the matter of the fact, it can be stored in a simple text file. Sometimes the data is not even in the traditional format as we assume, it may be in the form of video, SMS, pdf or something we might have not thought about it. It is the need of the organization to arrange it and make it meaningful. It will be easy to do so if we have data in the same format, however it is not the case most of the time. The real world have data in many different formats and that is the challenge we need to overcome with the Big Data. This variety of the data represent  represent Big Data. Big Data in Simple Words Big Data is not just about lots of data, it is actually a concept providing an opportunity to find new insight into your existing data as well guidelines to capture and analysis your future data. It makes any business more agile and robust so it can adapt and overcome business challenges. Tomorrow In tomorrow’s blog post we will try to answer discuss Evolution of Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• The Data Scientist

  - by BuckWoody

  A new term - well, perhaps not that new - has come up and I’m actually very excited about it. The term is Data Scientist, and since it’s new, it’s fairly undefined. I’ll explain what I think it means, and why I’m excited about it. In general, I’ve found the term deals at its most basic with analyzing data. Of course, we all do that, and the term itself in that definition is redundant. There is no science that I know of that does not work with analyzing lots of data. But the term seems to refer to more than the common practices of looking at data visually, putting it in a spreadsheet or report, or even using simple coding to examine data sets. The term Data Scientist (as far as I can make out this early in it’s use) is someone who has a strong understanding of data sources, relevance (statistical and otherwise) and processing methods as well as front-end displays of large sets of complicated data. Some - but not all - Business Intelligence professionals have these skills. In other cases, senior developers, database architects or others fill these needs, but in my experience, many lack the strong mathematical skills needed to make these choices properly. I’ve divided the knowledge base for someone that would wear this title into three large segments. It remains to be seen if a given Data Scientist would be responsible for knowing all these areas or would specialize. There are pretty high requirements on the math side, specifically in graduate-degree level statistics, but in my experience a company will only have a few of these folks, so they are expected to know quite a bit in each of these areas. Persistence The first area is finding, cleaning and storing the data. In some cases, no cleaning is done prior to storage - it’s just identified and the cleansing is done in a later step. This area is where the professional would be able to tell if a particular data set should be stored in a Relational Database Management System (RDBMS), across a set of key/value pair storage (NoSQL) or in a file system like HDFS (part of the Hadoop landscape) or other methods. Or do you examine the stream of data without storing it in another system at all? This is an important decision - it’s a foundation choice that deals not only with a lot of expense of purchasing systems or even using Cloud Computing (PaaS, SaaS or IaaS) to source it, but also the skillsets and other resources needed to care and feed the system for a long time. The Data Scientist sets something into motion that will probably outlast his or her career at a company or organization. Often these choices are made by senior developers, database administrators or architects in a company. But sometimes each of these has a certain bias towards making a decision one way or another. The Data Scientist would examine these choices in light of the data itself, starting perhaps even before the business requirements are created. The business may not even be aware of all the strategic and tactical data sources that they have access to. Processing Once the decision is made to store the data, the next set of decisions are based around how to process the data. An RDBMS scales well to a certain level, and provides a high degree of ACID compliance as well as offering a well-known set-based language to work with this data. In other cases, scale should be spread among multiple nodes (as in the case of Hadoop landscapes or NoSQL offerings) or even across a Cloud provider like Windows Azure Table Storage. In fact, in many cases - most of the ones I’m dealing with lately - the data should be split among multiple types of processing environments. This is a newer idea. Many data professionals simply pick a methodology (RDBMS with Star Schemas, NoSQL, etc.) and put all data there, regardless of its shape, processing needs and so on. A Data Scientist is familiar not only with the various processing methods, but how they work, so that they can choose the right one for a given need. This is a huge time commitment, hence the need for a dedicated title like this one. Presentation This is where the need for a Data Scientist is most often already being filled, sometimes with more or less success. The latest Business Intelligence systems are quite good at allowing you to create amazing graphics - but it’s the data behind the graphics that are the most important component of truly effective displays. This is where the mathematics requirement of the Data Scientist title is the most unforgiving. In fact, someone without a good foundation in statistics is not a good candidate for creating reports. Even a basic level of statistics can be dangerous. Anyone who works in analyzing data will tell you that there are multiple errors possible when data just seems right - and basic statistics bears out that you’re on the right track - that are only solvable when you understanding why the statistical formula works the way it does. And there are lots of ways of presenting data. Sometimes all you need is a “yes” or “no” answer that can only come after heavy analysis work. In that case, a simple e-mail might be all the reporting you need. In others, complex relationships and multiple components require a deep understanding of the various graphical methods of presenting data. Knowing which kind of chart, color, graphic or shape conveys a particular datum best is essential knowledge for the Data Scientist. Why I’m excited I love this area of study. I like math, stats, and computing technologies, but it goes beyond that. I love what data can do - how it can help an organization. I’ve been fortunate enough in my professional career these past two decades to work with lots of folks who perform this role at companies from aerospace to medical firms, from manufacturing to retail. Interestingly, the size of the company really isn’t germane here. I worked with one very small bio-tech (cryogenics) company that worked deeply with analysis of complex interrelated data. So  watch this space. No, I’m not leaving Azure or distributed computing or Microsoft. In fact, I think I’m perfectly situated to investigate this role further. We have a huge set of tools, from RDBMS to Hadoop to allow me to explore. And I’m happy to share what I learn along the way.

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