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  • SQL Developer Debugging, Watches, Smart Data, & Data

    - by thatjeffsmith
    After presenting the SQL Developer PL/SQL debugger for about an hour yesterday at KScope12 in San Antonio, my boss came up and asked, “Now, would you really want to know what the Smart Data panel does?” Apparently I had ‘made up’ my own story about what that panel’s intent is based on my experience with it. Not good Jeff, not good. It was a very small point of my presentation, but I probably should have read the docs. The Smart Data tab displays information about variables, using your Debugger: Smart Data preferences. You can also specify these preferences by right-clicking in the Smart Data window and selecting Preferences. Debugger Smart Data Preferences, control number of variables to display The Smart Data panel auto-inspects the last X accessed variables. So if you have a program with 26 variables, instead of showing you all 26, it will just show you the last two variables that were referenced in your program. If you were to click on the ‘Data’ debug panel, you’ll see EVERYTHING. And if you only want to see a very specific set of values, then you should use Watches. The Smart Data Panel As I step through the code, the variables being tracked change as they are referenced. Only the most recent ones display. This is controlled by the ‘Maximum Locations to Remember’ preference. Step through the code, see the latest variables accessed The Data Panel All variables are displayed. Might be information overload on large PL/SQL programs where you have many dozens or even hundreds of variables to track. Shows everything all the time Watches Watches are added manually and only show what you ask for. Data on Demand – add a watch to track a specific variable Remember, you can interact with your data If you want to do more than just watch, you can mouse-right on a data element, and change the value of the variable as the program is running. This is one of the primary benefits to debugging over using DBMS_OUTPUT to track what’s happening in your program. Change the values while the program is running to test your ‘What if?’ scenarios

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  • SQL – Biggest Concerns in a Data-Driven World

    - by Pinal Dave
    The ongoing chaos over Government Agency’s snooping has ignited a heated debate on privacy of personal data and its use by government and/or other institutions. It has created a feeling of disapproval and distrust among users. This incident proves to be a lesson for companies that are looking to leverage their business using a data driven approach. According to analysts, the goal of gathering personal information should be to deliver benefits to both the parties – the user as well as the data collector(government or business). Using data the right way is crucial, and companies need to deploy the right software applications and systems to ensure that their efforts are well-directed. However, there are various issues plaguing analysts regarding available software, which are highlighted below. According to a InformationWeek 2013 Survey of Analytics, Business Intelligence and Information Management where 541 business technology professionals contributed as respondents, it was discovered that the biggest concern was deemed to be the scarcity of expertise and high costs associated with the same. This concern was voiced by as many as 38% of the participants. A close second came out to be the issue of data warehouse appliance platforms being expensive, with 33% of those present believing it to be a huge roadblock. Another revelation made in this respect was that 31% professionals weren’t even sure how Data Analytics can create business opportunities for them. Another 17% shared that they found data platform technologies such as Hadoop and NoSQL technologies hard to learn. These results clearly pointed out that there are awareness and expertise issues that also need much attention. Unless the demand-supply gap of Business Intelligence professionals well versed in data analysis technologies is met, this divide is going to affect how companies make the most of their BI campaigns. One of the key action points that can be taken to salvage the situation, is to provide training on Data Analytics concepts. Koenig Solutions offer courses on many such technologies including a course on MCSE SQL Server 2012: BI Platform. So it’s time to brush up your skills and get down to work in a data driven world that awaits you ahead. 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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  • Store XML data in Core Data

    - by ct2k7
    Hi, is there any easy way of store XML data into core data? Currently, my app just pulls the values from the XML file directly, however, this isn't efficient for XML files which holds over 100 entries, thus storing the data in Core Data would be the best option. XML file is called/downloaded/parsed ever time the app opens. With the Core Data, the XML data would be downloaded ever 3600 seconds or so, and refresh the current data in the core data, to reduce the loading time when opening the app. Any ideas on how I can do this? Having reviewed the developer documentation, it doesn't look very tasty.

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  • Where can I find free and open data?

    - by kitsune
    Sooner or later, coders will feel the need to have access to "open data" in one of their projects, from knowing a city's zip to a more obscure information such as the axial tilt of Pluto. I know data.un.org which offers access to the UN's extensive array of databases that deal with human development and other socio-economic issues. The other usual suspects are NASA and the USGS for planetary data. There's an article at readwriteweb with more links. infochimps.org seems to stand out. Personally, I need to find historic commodity prices, stock values and other financial data. All these data sets seem to cost money however. Clarification To clarify, I'm interested in all kinds of open data, because sooner or later, I know I will be in a situation where I could need it. I will try to edit this answer and include the suggestions in a structured manners. A link for financial data was hidden in that readwriteweb article, doh! It's called opentick.com. Looks good so far! Update I stumbled over semantic data in another question of mine on here. There is opencyc ('the world's largest and most complete general knowledge base and commonsense reasoning engine'). A project called UMBEL provides a light-weight, distilled version of opencyc. Umbel has semantic data in rdf/owl/skos n3 syntax. The Worldbank also released a very nice API. It offers data from the last 50 years for about 200 countries

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  • Temporary storage for keeping data between program iterations?

    - by mr.b
    I am working on an application that works like this: It fetches data from many sources, resulting in pool of about 500,000-1,500,000 records (depends on time/day) Data is parsed Part of data is processed in a way to compare it to pre-existing data (read from database), calculations are made, and stored in database. Resulting dataset that has to be stored in database is, however, much smaller in size (compared to original data set), and ranges from 5,000-50,000 records. This process almost always updates existing data, perhaps adds few more records. Then, data from step 2 should be kept somehow, somewhere, so that next time data is fetched, there is a data set which can be used to perform calculations, without touching pre-existing data in database. I should point out that this data can be lost, it's not irreplaceable (key information can be read from database if needed), but it would speed up the process next time. Application components can (and will be) run off different computers (in the same network), so storage has to be reachable from multiple hosts. I have considered using memcached, but I'm not quite sure should I do so, because one record is usually no smaller than 200 bytes, and if I have 1,500,000 records, I guess that it would amount to over 300 MB of memcached cache... But that doesn't seem scalable to me - what if data was 5x that amount? If it were to consume 1-2 GB of cache only to keep data in between iterations (which could easily happen)? So, the question is: which temporary storage mechanism would be most suitable for this kind of processing? I haven't considered using mysql temporary tables, as I'm not sure if they can persist between sessions, and be used by other hosts in network... Any other suggestion? Something I should consider?

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  • Why are data structures so important in interviews?

    - by Vamsi Emani
    I am a newbie into the corporate world recently graduated in computers. I am a java/groovy developer. I am a quick learner and I can learn new frameworks, APIs or even programming languages within considerably short amount of time. Albeit that, I must confess that I was not so strong in data structures when I graduated out of college. Through out the campus placements during my graduation, I've witnessed that most of the biggie tech companies like Amazon, Microsoft etc focused mainly on data structures. It appears as if data structures is the only thing that they expect from a graduate. Adding to this, I see that there is this general perspective that a good programmer is necessarily a one with good knowledge about data structures. To be honest, I felt bad about that. I write good code. I follow standard design patterns of coding, I do use data structures but at the superficial level as in java exposed APIs like ArrayLists, LinkedLists etc. But the companies usually focused on the intricate aspects of Data Structures like pointer based memory manipulation and time complexities. Probably because of my java-ish background, Back then, I understood code efficiency and logic only when talked in terms of Object Oriented Programming like Objects, instances, etc but I never drilled down into the level of bits and bytes. I did not want people to look down upon me for this knowledge deficit of mine in Data Structures. So really why all this emphasis on Data Structures? Does, Not having knowledge in Data Structures really effect one's career in programming? Or is the knowledge in this subject really a sufficient basis to differentiate a good and a bad programmer?

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  • Data structure for pattern matching.

    - by alvonellos
    Let's say you have an input file with many entries like these: date, ticker, open, high, low, close, <and some other values> And you want to execute a pattern matching routine on the entries(rows) in that file, using a candlestick pattern, for example. (See, Doji) And that pattern can appear on any uniform time interval (let t = 1s, 5s, 10s, 1d, 7d, 2w, 2y, and so on...). Say a pattern matching routine can take an arbitrary number of rows to perform an analysis and contain an arbitrary number of subpatterns. In other words, some patterns may require 4 entries to operate on. Say also that the routine (may) later have to find and classify extrema (local and global maxima and minima as well as inflection points) for the ticker over a closed interval, for example, you could say that a cubic function (x^3) has the extrema on the interval [-1, 1]. (See link) What would be the most natural choice in terms of a data structure? What about an interface that conforms a Ticker object containing one row of data to a collection of Ticker so that an arbitrary pattern can be applied to the data. What's the first thing that comes to mind? I chose a doubly-linked circular linked list that has the following methods: push_front() push_back() pop_front() pop_back() [] //overloaded, can be used with negative parameters But that data structure seems very clumsy, since so much pushing and popping is going on, I have to make a deep copy of the data structure before running an analysis on it. So, I don't know if I made my question very clear -- but the main points are: What kind of data structures should be considered when analyzing sequential data points to conform to a pattern that does NOT require random access? What kind of data structures should be considered when classifying extrema of a set of data points?

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  • Five Key Strategies in Master Data Management

    - by david.butler(at)oracle.com
    Here is a very interesting Profit Magazine article on MDM: A recent customer survey reveals the deleterious effects of data fragmentation. by Trevor Naidoo, December 2010   Across industries and geographies, IT organizations have grown in complexity, whether due to mergers and acquisitions, or decentralized systems supporting functional or departmental requirements. With systems architected over time to support unique, one-off process needs, they are becoming costly to maintain, and the Internet has only further added to the complexity. Data fragmentation has become a key inhibitor in delivering flexible, user-friendly systems. The Oracle Insight team conducted a survey assessing customers' master data management (MDM) capabilities over the past two years to get a sense of where they are in terms of their capabilities. The responses, by 27 respondents from six different industries, reveal five key areas in which customers need to improve their data management in order to get better financial results. 1. Less than 15 percent of organizations surveyed understand the sources and quality of their master data, and have a roadmap to address missing data domains. Examples of the types of master data domains referred to are customer, supplier, product, financial and site. Many organizations have multiple sources of master data with varying degrees of data quality in each source -- customer data stored in the customer relationship management system is inconsistent with customer data stored in the order management system. Imagine not knowing how many places you stored your customer information, and whether a customer's address was the most up to date in each source. In fact, more than 55 percent of the respondents in the survey manage their data quality on an ad-hoc basis. It is important for organizations to document their inventory of data sources and then profile these data sources to ensure that there is a consistent definition of key data entities throughout the organization. Some questions to ask are: How do we define a customer? What is a product? How do we define a site? The goal is to strive for one common repository for master data that acts as a cross reference for all other sources and ensures consistent, high-quality master data throughout the organization. 2. Only 18 percent of respondents have an enterprise data management strategy to ensure that data is treated as an asset to the organization. Most respondents handle data at the department or functional level and do not have an enterprise view of their master data. The sales department may track all their interactions with customers as they move through the sales cycle, the service department is tracking their interactions with the same customers independently, and the finance department also has a different perspective on the same customer. The salesperson may not be aware that the customer she is trying to sell to is experiencing issues with existing products purchased, or that the customer is behind on previous invoices. The lack of a data strategy makes it difficult for business users to turn data into information via reports. Without the key building blocks in place, it is difficult to create key linkages between customer, product, site, supplier and financial data. These linkages make it possible to understand patterns. A well-defined data management strategy is aligned to the business strategy and helps create the governance needed to ensure that data stewardship is in place and data integrity is intact. 3. Almost 60 percent of respondents have no strategy to integrate data across operational applications. Many respondents have several disparate sources of data with no strategy to keep them in sync with each other. Even though there is no clear strategy to integrate the data (see #2 above), the data needs to be synced and cross-referenced to keep the business processes running. About 55 percent of respondents said they perform this integration on an ad hoc basis, and in many cases, it is done manually with the help of Microsoft Excel spreadsheets. For example, a salesperson needs a report on global sales for a specific product, but the product has different product numbers in different countries. Typically, an analyst will pull all the data into Excel, manually create a cross reference for that product, and then aggregate the sales. The exact same procedure has to be followed if the same report is needed the following month. A well-defined consolidation strategy will ensure that a central cross-reference is maintained with updates in any one application being propagated to all the other systems, so that data is synchronized and up to date. This can be done in real time or in batch mode using integration technology. 4. Approximately 50 percent of respondents spend manual efforts cleansing and normalizing data. Information stored in various systems usually follows different standards and formats, making it difficult to match the data. A customer's address can be stored in different ways using a variety of abbreviations -- for example, "av" or "ave" for avenue. Similarly, a product's attributes can be stored in a number of different ways; for example, a size attribute can be stored in inches and can also be entered as "'' ". These types of variations make it difficult to match up data from different sources. Today, most customers rely on manual, heroic efforts to match, cleanse, and de-duplicate data -- clearly not a scalable, sustainable model. To solve this challenge, organizations need the ability to standardize data for customers, products, sites, suppliers and financial accounts; however, less than 10 percent of respondents have technology in place to automatically resolve duplicates. It is no wonder, therefore, that we get communications about products we don't own, at addresses we don't reside, and using channels (like direct mail) we don't like. An all-too-common example of a potential challenge follows: Customers end up receiving duplicate communications, which not only impacts customer satisfaction, but also incurs additional mailing costs. Cleansing, normalizing, and standardizing data will help address most of these issues. 5. Only 10 percent of respondents have the ability to share data that was mastered in a master data hub. Close to 60 percent of respondents have efforts in place that profile, standardize and cleanse data manually, and the output of these efforts are stored in spreadsheets in various parts of the organization. This valuable information is not easily shared with the rest of the organization and, more importantly, this enriched information cannot be sent back to the source systems so that the data is fixed at the source. A key benefit of a master data management strategy is not only to clean the data, but to also share the data back to the source systems as well as other systems that need the information. Aside from the source systems, another key beneficiary of this data is the business intelligence system. Having clean master data as input to business intelligence systems provides more accurate and enhanced reporting.  Characteristics of Stellar MDM When deciding on the right master data management technology, organizations should look for solutions that have four main characteristics: enterprise-grade MDM performance complete technology that can be rapidly deployed and addresses multiple business issues end-to-end MDM process management with data quality monitoring and assurance pre-built MDM business relevant applications with data stores and workflows These master data management capabilities will aid in moving closer to a best-practice maturity level, delivering tremendous efficiencies and savings as well as revenue growth opportunities as a result of better understanding your customers.  Trevor Naidoo is a senior director in Industry Strategy and Insight at Oracle. 

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  • How to Stream Videos and Music Over the Network Using VLC

    - by Chris Hoffman
    VLC includes a fairly easy-to-use streaming feature that can stream music and videos over a local network or the Internet. You can tune into the stream using VLC or other media players. Use VLC’s web interface as a remote control to control the stream from elsewhere. Bear in mind that you may not have the bandwidth to stream high-definition videos over the Internet, though. How to Use an Xbox 360 Controller On Your Windows PC Download the Official How-To Geek Trivia App for Windows 8 How to Banish Duplicate Photos with VisiPic

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  • Address Regulatory Mandates for Data Encryption Without Changing Your Applications

    - by Troy Kitch
    The Payment Card Industry Data Security Standard, US state-level data breach laws, and numerous data privacy regulations worldwide all call for data encryption to protect personally identifiable information (PII). However encrypting PII data in applications requires costly and complex application changes. Fortunately, since this data typically resides in the application database, using Oracle Advanced Security, PII can be encrypted transparently by the Oracle database without any application changes. In this ISACA webinar, learn how Oracle Advanced Security offers complete encryption for data at rest, in transit, and on backups, along with built-in key management to help organizations meet regulatory requirements and save money. You will also hear from TransUnion Interactive, the consumer subsidiary of TransUnion, a global leader in credit and information management, which maintains credit histories on an estimated 500 million consumers across the globe, about how they addressed PCI DSS encryption requirements using Oracle Database 11g with Oracle Advanced Security. Register to watch the webinar now.

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  • Using Hadooop (HDInsight) with Microsoft - Two (OK, Three) Options

    - by BuckWoody
    Microsoft has many tools for “Big Data”. In fact, you need many tools – there’s no product called “Big Data Solution” in a shrink-wrapped box – if you find one, you probably shouldn’t buy it. It’s tempting to want a single tool that handles everything in a problem domain, but with large, complex data, that isn’t a reality. You’ll mix and match several systems, open and closed source, to solve a given problem. But there are tools that help with handling data at large, complex scales. Normally the best way to do this is to break up the data into parts, and then put the calculation engines for that chunk of data right on the node where the data is stored. These systems are in a family called “Distributed File and Compute”. Microsoft has a couple of these, including the High Performance Computing edition of Windows Server. Recently we partnered with Hortonworks to bring the Apache Foundation’s release of Hadoop to Windows. And as it turns out, there are actually two (technically three) ways you can use it. (There’s a more detailed set of information here: http://www.microsoft.com/sqlserver/en/us/solutions-technologies/business-intelligence/big-data.aspx, I’ll cover the options at a general level below)  First Option: Windows Azure HDInsight Service  Your first option is that you can simply log on to a Hadoop control node and begin to run Pig or Hive statements against data that you have stored in Windows Azure. There’s nothing to set up (although you can configure things where needed), and you can send the commands, get the output of the job(s), and stop using the service when you are done – and repeat the process later if you wish. (There are also connectors to run jobs from Microsoft Excel, but that’s another post)   This option is useful when you have a periodic burst of work for a Hadoop workload, or the data collection has been happening into Windows Azure storage anyway. That might be from a web application, the logs from a web application, telemetrics (remote sensor input), and other modes of constant collection.   You can read more about this option here:  http://blogs.msdn.com/b/windowsazure/archive/2012/10/24/getting-started-with-windows-azure-hdinsight-service.aspx Second Option: Microsoft HDInsight Server Your second option is to use the Hadoop Distribution for on-premises Windows called Microsoft HDInsight Server. You set up the Name Node(s), Job Tracker(s), and Data Node(s), among other components, and you have control over the entire ecostructure.   This option is useful if you want to  have complete control over the system, leave it running all the time, or you have a huge quantity of data that you have to bulk-load constantly – something that isn’t going to be practical with a network transfer or disk-mailing scheme. You can read more about this option here: http://www.microsoft.com/sqlserver/en/us/solutions-technologies/business-intelligence/big-data.aspx Third Option (unsupported): Installation on Windows Azure Virtual Machines  Although unsupported, you could simply use a Windows Azure Virtual Machine (we support both Windows and Linux servers) and install Hadoop yourself – it’s open-source, so there’s nothing preventing you from doing that.   Aside from being unsupported, there are other issues you’ll run into with this approach – primarily involving performance and the amount of configuration you’ll need to do to access the data nodes properly. But for a single-node installation (where all components run on one system) such as learning, demos, training and the like, this isn’t a bad option. Did I mention that’s unsupported? :) You can learn more about Windows Azure Virtual Machines here: http://www.windowsazure.com/en-us/home/scenarios/virtual-machines/ And more about Hadoop and the installation/configuration (on Linux) here: http://en.wikipedia.org/wiki/Apache_Hadoop And more about the HDInsight installation here: http://www.microsoft.com/web/gallery/install.aspx?appid=HDINSIGHT-PREVIEW Choosing the right option Since you have two or three routes you can go, the best thing to do is evaluate the need you have, and place the workload where it makes the most sense.  My suggestion is to install the HDInsight Server locally on a test system, and play around with it. Read up on the best ways to use Hadoop for a given workload, understand the parts, write a little Pig and Hive, and get your feet wet. Then sign up for a test account on HDInsight Service, and see how that leverages what you know. If you're a true tinkerer, go ahead and try the VM route as well. Oh - there’s another great reference on the Windows Azure HDInsight that just came out, here: http://blogs.msdn.com/b/brunoterkaly/archive/2012/11/16/hadoop-on-azure-introduction.aspx  

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  • Oracle - A Leader in Gartner's MQ for Master Data Management for Customer Data

    - by Mala Narasimharajan
      The Gartner MQ report for Master Data Management of Customer Data Solutions is released and we're proud to say that Oracle is in the leaders' quadrant.  Here's a snippet from the report itself:  " “Oracle has a strong, though complex, portfolio of domain-specific MDM products that include prepackaged data models. Gartner estimates that Oracle now has over 1,500 licensed MDM customers, including 650 customers managing customer data. The MDM portfolio includes three products that address MDM of customer data solution needs: Oracle Fusion Customer Hub (FCH), Oracle CDH and Oracle Siebel UCM. These three MDM products are positioned for different segments of the market and Oracle is progressively moving all three products onto a common MDM technology platform..." (Gartner, Oct 18, 2012)  For more information on Oracle's solutions for customer data in Master Data Management, click here.  

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  • Almost Realtime Data and Web application

    - by Chris G.
    I have a computer that is recording 100 different data points into an OPC server. I've written a simple OPC client that can read all of this data. I have a front-end website on a different network that I would like to consume this data. I could easily set the OPC client to send the data to a SQL server and the website could read from it, but that would be a lot of writes. If I wanted the data to be updated every 10 seconds I'd be writing to the database every 10 seconds. (I could probably just serialize the 100 points to get 1 write / 10 seconds but that would also limit my ability to search the data later). This solution wouldn't scale very well. If I had 100 of these computers the situation would quickly grow out of hand. Obviously I am well out of my league here and I have no experience with working with a large amount of data like this. What are my options and what should I research?

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  • SQL SERVER – Integrate Your Data with Skyvia – Cloud ETL Solution

    - by Pinal Dave
    In our days data integration often becomes a key aspect of business success. For business analysts it’s very important to get integrated data from various sources, such as relational databases, cloud CRMs, etc. to make correct and successful decisions. There are various data integration solutions on market, and today I will tell about one of them – Skyvia. Skyvia is a cloud data integration service, which allows integrating data in cloud CRMs and different relational databases. It is a completely online solution and does not require anything except for a browser. Skyvia provides powerful etl tools for data import, export, replication, and synchronization for SQL Server and other databases and cloud CRMs. You can use Skyvia data import tools to load data from various sources to SQL Server (and SQL Azure). Skyvia supports such cloud CRMs as Salesforce and Microsoft Dynamics CRM and such databases as MySQL and PostgreSQL. You even can migrate data from SQL Server to SQL Server, or from SQL Server to other databases and cloud CRMs. Additionally Skyvia supports import of CSV files, either uploaded manually or stored on cloud file storage services, such as Dropbox, Box, Google Drive, or FTP servers. When data import is not enough, Skyvia offers bidirectional data synchronization. With this tool, you can synchronize SQL Server data with other databases and cloud CRMs. After performing the first synchronization, Skyvia tracks data changes in the synchronized data storages. In SQL Server databases (and other relational databases) it creates additional tracking tables and triggers. This allows synchronizing only the changed data. Skyvia also maps records by their primary key values to each other, so it does not require different sources to have the same primary key structure. It still can match the corresponding records without having to add any additional columns or changing data structure. The only requirement for synchronization is that primary keys must be autogenerated. With Skyvia it’s not necessary for data to have the same structure in integrated data storages. Skyvia supports powerful mapping mechanisms that allow synchronizing data with completely different structure. It provides support for complex mathematical and string expressions when mapping data, using lookups, etc. You may use data splitting – loading data from a single CSV file or source table to multiple related target tables. Or you may load data from several source CSV files or tables to several related target tables. In each case Skyvia preserves data relations. It builds corresponding relations between the target data automatically. When you often work with cloud CRM data, native CRM data reporting and analysis tools may be not enough for you. And there is a vast set of professional data analysis and reporting tools available for SQL Server. With Skyvia you can quickly copy your cloud CRM data to an SQL Server database and apply corresponding SQL Server tools to the data. In such case you can use Skyvia data replication tools. It allows you to quickly copy cloud CRM data to SQL Server or other databases without customizing any mapping. You need just to specify columns to copy data from. Target database tables will be created automatically. Skyvia offers powerful filtering settings to replicate only the records you need. Skyvia also provides capability to export data from SQL Server (including SQL Azure) and other databases and cloud CRMs to CSV files. These files can be either downloadable manually or loaded to cloud file storages or FTP server. You can use export, for example, to backup SQL Azure data to Dropbox. Any data integration operation can be scheduled for automatic execution. Thus, you can automate your SQL Azure data backup or data synchronization – just configure it once, then schedule it, and benefit from automatic data integration with Skyvia. Currently registration and using Skyvia is completely free, so you can try it yourself and find out whether its data migration and integration tools suits for you. Visit this link to register on Skyvia: https://app.skyvia.com/register Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL Tagged: Cloud Computing

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  • SQL SERVER – Why Do We Need Master Data Management – Importance and Significance of Master Data Management (MDM)

    - by pinaldave
    Let me paint a picture of everyday life for you.  Let’s say you and your wife both have address books for your groups of friends.  There is definitely overlap between them, so that you both have the addresses for your mutual friends, and there are addresses that only you know, and some only she knows.  They also might be organized differently.  You might list your friend under “J” for “Joe” or even under “W” for “Work,” while she might list him under “S” for “Joe Smith” or under your name because he is your friend.  If you happened to trade, neither of you would be able to find anything! This is where data management would be very important.  If you were to consolidate into one address book, you would have to set rules about how to organize the book, and both of you would have to follow them.  You would also make sure that poor Joe doesn’t get entered twice under “J” and under “S.” This might be a familiar situation to you, whether you are thinking about address books, record collections, books, or even shopping lists.  Wherever there is a lot of data to consolidate, you are going to run into problems unless everyone is following the same rules. I’m sure that my readers can figure out where I am going with this.  What is SQL Server but a computerized way to organize data?  And Microsoft is making it easier and easier to get all your “addresses” into one place.  In the  2008 version of SQL they introduced a new tool called Master Data Services (MDS) for Master Data Management, and they have improved it for the new 2012 version. MDM was hailed as a major improvement for business intelligence.  You might not think that an organizational system is terribly exciting, but think about the kind of “address books” a company might have.  Many companies have lots of important information, like addresses, credit card numbers, purchase history, and so much more.  To organize all this efficiently so that customers are well cared for and properly billed (only once, not never or multiple times!) is a major part of business intelligence. MDM comes into play because it will comb through these mountains of data and make sure that all the information is consistent, accurate, and all placed in one database so that employees don’t have to search high and low and waste their time. MDM also has operational MDM functions.  This is not a redundancy.  Operational MDM means that when one employee updates one bit of information in the database, for example – updating a new address for a customer, operational MDM ensures that this address is updated throughout the system so that all departments will have the correct information. Another cool thing about MDM is that it features Master Data Services Configuration Manager, which is exactly what it sounds like.  It has a built-in “helper” that lets you set up your database quickly, easily, and with the correct configurations.  While talking about cool features, I can’t skip over the add-in for Excel.  This allows you to link certain data to Excel files for easier sharing and uploading. In summary, I want to emphasize that the scariest part of the database is slowly disappearing.  Everyone knows that a database – one consolidated area for all your data – is a good idea, but the idea of setting one up is daunting.  But SQL Server is making data management easier and easier with features like Master Data Services (MDS). Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: Master Data Services, MDM

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  • Data breakpoints to find points where data gets broken

    - by raccoon_tim
    When working with a large code base, finding reasons for bizarre bugs can often be like finding a needle in a hay stack. Finding out why an object gets corrupted without no apparent reason can be quite daunting, especially when it seems to happen randomly and totally out of context. Scenario Take the following scenario as an example. You have defined the a class that contains an array of characters that is 256 characters long. You now implement a method for filling this buffer with a string passed as an argument. At this point you mistakenly expect the buffer to be 256 characters long. At some point you notice that you require another character buffer and you add that after the previous one in the class definition. You now figure that you don’t need the 256 characters that the first member can hold and you shorten that to 128 to conserve space. At this point you should start thinking that you also have to modify the method defined above to safeguard against buffer overflow. It so happens, however, that in this not so perfect world this does not cross your mind. Buffer overflow is one of the most frequent sources for errors in a piece of software and often one of the most difficult ones to detect, especially when data is read from an outside source. Many mass copy functions provided by the C run-time provide versions that have boundary checking (defined with the _s suffix) but they can not guard against hard coded buffer lengths that at some point get changed. Finding the bug Getting back to the scenario, you’re now wondering why does the second string get modified with data that makes no sense at all. Luckily, Visual Studio provides you with a tool to help you with finding just these kinds of errors. It’s called data breakpoints. To add a data breakpoint, you first run your application in debug mode or attach to it in the usual way, and then go to Debug, select New Breakpoint and New Data Breakpoint. In the popup that opens, you can type in the memory address and the amount of bytes you wish to monitor. You can also use an expression here, but it’s often difficult to come up with an expression for data in an object allocated on the heap when not in the context of a certain stack frame. There are a couple of things to note about data breakpoints, however. First of all, Visual Studio supports a maximum of four data breakpoints at any given time. Another important thing to notice is that some C run-time functions modify memory in kernel space which does not trigger the data breakpoint. For instance, calling ReadFile on a buffer that is monitored by a data breakpoint will not trigger the breakpoint. The application will now break at the address you specified it to. Often you might immediately spot the issue but the very least this feature can do is point you in the right direction in search for the real reason why the memory gets inadvertently modified. Conclusions Data breakpoints are a great feature, especially when doing a lot of low level operations where multiple locations modify the same data. With the exception of some special cases, like kernel memory modification, you can use it whenever you need to check when memory at a certain location gets changed on purpose or inadvertently.

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  • Capturing and Transforming ASP.NET Output with Response.Filter

    - by Rick Strahl
    During one of my Handlers and Modules session at DevConnections this week one of the attendees asked a question that I didn’t have an immediate answer for. Basically he wanted to capture response output completely and then apply some filtering to the output – effectively injecting some additional content into the page AFTER the page had completely rendered. Specifically the output should be captured from anywhere – not just a page and have this code injected into the page. Some time ago I posted some code that allows you to capture ASP.NET Page output by overriding the Render() method, capturing the HtmlTextWriter() and reading its content, modifying the rendered data as text then writing it back out. I’ve actually used this approach on a few occasions and it works fine for ASP.NET pages. But this obviously won’t work outside of the Page class environment and it’s not really generic – you have to create a custom page class in order to handle the output capture. [updated 11/16/2009 – updated ResponseFilterStream implementation and a few additional notes based on comments] Enter Response.Filter However, ASP.NET includes a Response.Filter which can be used – well to filter output. Basically Response.Filter is a stream through which the OutputStream is piped back to the Web Server (indirectly). As content is written into the Response object, the filter stream receives the appropriate Stream commands like Write, Flush and Close as well as read operations although for a Response.Filter that’s uncommon to be hit. The Response.Filter can be programmatically replaced at runtime which allows you to effectively intercept all output generation that runs through ASP.NET. A common Example: Dynamic GZip Encoding A rather common use of Response.Filter hooking up code based, dynamic  GZip compression for requests which is dead simple by applying a GZipStream (or DeflateStream) to Response.Filter. The following generic routines can be used very easily to detect GZip capability of the client and compress response output with a single line of code and a couple of library helper routines: WebUtils.GZipEncodePage(); which is handled with a few lines of reusable code and a couple of static helper methods: /// <summary> ///Sets up the current page or handler to use GZip through a Response.Filter ///IMPORTANT:  ///You have to call this method before any output is generated! /// </summary> public static void GZipEncodePage() {     HttpResponse Response = HttpContext.Current.Response;     if(IsGZipSupported())     {         stringAcceptEncoding = HttpContext.Current.Request.Headers["Accept-Encoding"];         if(AcceptEncoding.Contains("deflate"))         {             Response.Filter = newSystem.IO.Compression.DeflateStream(Response.Filter,                                        System.IO.Compression.CompressionMode.Compress);             Response.AppendHeader("Content-Encoding", "deflate");         }         else        {             Response.Filter = newSystem.IO.Compression.GZipStream(Response.Filter,                                       System.IO.Compression.CompressionMode.Compress);             Response.AppendHeader("Content-Encoding", "gzip");                            }     }     // Allow proxy servers to cache encoded and unencoded versions separately    Response.AppendHeader("Vary", "Content-Encoding"); } /// <summary> /// Determines if GZip is supported /// </summary> /// <returns></returns> public static bool IsGZipSupported() { string AcceptEncoding = HttpContext.Current.Request.Headers["Accept-Encoding"]; if (!string.IsNullOrEmpty(AcceptEncoding) && (AcceptEncoding.Contains("gzip") || AcceptEncoding.Contains("deflate"))) return true; return false; } GZipStream and DeflateStream are streams that are assigned to Response.Filter and by doing so apply the appropriate compression on the active Response. Response.Filter content is chunked So to implement a Response.Filter effectively requires only that you implement a custom stream and handle the Write() method to capture Response output as it’s written. At first blush this seems very simple – you capture the output in Write, transform it and write out the transformed content in one pass. And that indeed works for small amounts of content. But you see, the problem is that output is written in small buffer chunks (a little less than 16k it appears) rather than just a single Write() statement into the stream, which makes perfect sense for ASP.NET to stream data back to IIS in smaller chunks to minimize memory usage en route. Unfortunately this also makes it a more difficult to implement any filtering routines since you don’t directly get access to all of the response content which is problematic especially if those filtering routines require you to look at the ENTIRE response in order to transform or capture the output as is needed for the solution the gentleman in my session asked for. So in order to address this a slightly different approach is required that basically captures all the Write() buffers passed into a cached stream and then making the stream available only when it’s complete and ready to be flushed. As I was thinking about the implementation I also started thinking about the few instances when I’ve used Response.Filter implementations. Each time I had to create a new Stream subclass and create my custom functionality but in the end each implementation did the same thing – capturing output and transforming it. I thought there should be an easier way to do this by creating a re-usable Stream class that can handle stream transformations that are common to Response.Filter implementations. Creating a semi-generic Response Filter Stream Class What I ended up with is a ResponseFilterStream class that provides a handful of Events that allow you to capture and/or transform Response content. The class implements a subclass of Stream and then overrides Write() and Flush() to handle capturing and transformation operations. By exposing events it’s easy to hook up capture or transformation operations via single focused methods. ResponseFilterStream exposes the following events: CaptureStream, CaptureString Captures the output only and provides either a MemoryStream or String with the final page output. Capture is hooked to the Flush() operation of the stream. TransformStream, TransformString Allows you to transform the complete response output with events that receive a MemoryStream or String respectively and can you modify the output then return it back as a return value. The transformed output is then written back out in a single chunk to the response output stream. These events capture all output internally first then write the entire buffer into the response. TransformWrite, TransformWriteString Allows you to transform the Response data as it is written in its original chunk size in the Stream’s Write() method. Unlike TransformStream/TransformString which operate on the complete output, these events only see the current chunk of data written. This is more efficient as there’s no caching involved, but can cause problems due to searched content splitting over multiple chunks. Using this implementation, creating a custom Response.Filter transformation becomes as simple as the following code. To hook up the Response.Filter using the MemoryStream version event: ResponseFilterStream filter = new ResponseFilterStream(Response.Filter); filter.TransformStream += filter_TransformStream; Response.Filter = filter; and the event handler to do the transformation: MemoryStream filter_TransformStream(MemoryStream ms) { Encoding encoding = HttpContext.Current.Response.ContentEncoding; string output = encoding.GetString(ms.ToArray()); output = FixPaths(output); ms = new MemoryStream(output.Length); byte[] buffer = encoding.GetBytes(output); ms.Write(buffer,0,buffer.Length); return ms; } private string FixPaths(string output) { string path = HttpContext.Current.Request.ApplicationPath; // override root path wonkiness if (path == "/") path = ""; output = output.Replace("\"~/", "\"" + path + "/").Replace("'~/", "'" + path + "/"); return output; } The idea of the event handler is that you can do whatever you want to the stream and return back a stream – either the same one that’s been modified or a brand new one – which is then sent back to as the final response. The above code can be simplified even more by using the string version events which handle the stream to string conversions for you: ResponseFilterStream filter = new ResponseFilterStream(Response.Filter); filter.TransformString += filter_TransformString; Response.Filter = filter; and the event handler to do the transformation calling the same FixPaths method shown above: string filter_TransformString(string output) { return FixPaths(output); } The events for capturing output and capturing and transforming chunks work in a very similar way. By using events to handle the transformations ResponseFilterStream becomes a reusable component and we don’t have to create a new stream class or subclass an existing Stream based classed. By the way, the example used here is kind of a cool trick which transforms “~/” expressions inside of the final generated HTML output – even in plain HTML controls not HTML controls – and transforms them into the appropriate application relative path in the same way that ResolveUrl would do. So you can write plain old HTML like this: <a href=”~/default.aspx”>Home</a>  and have it turned into: <a href=”/myVirtual/default.aspx”>Home</a>  without having to use an ASP.NET control like Hyperlink or Image or having to constantly use: <img src=”<%= ResolveUrl(“~/images/home.gif”) %>” /> in MVC applications (which frankly is one of the most annoying things about MVC especially given the path hell that extension-less and endpoint-less URLs impose). I can’t take credit for this idea. While discussing the Response.Filter issues on Twitter a hint from Dylan Beattie who pointed me at one of his examples which does something similar. I thought the idea was cool enough to use an example for future demos of Response.Filter functionality in ASP.NET next I time I do the Modules and Handlers talk (which was great fun BTW). How practical this is is debatable however since there’s definitely some overhead to using a Response.Filter in general and especially on one that caches the output and the re-writes it later. Make sure to test for performance anytime you use Response.Filter hookup and make sure it' doesn’t end up killing perf on you. You’ve been warned :-}. How does ResponseFilterStream work? The big win of this implementation IMHO is that it’s a reusable  component – so for implementation there’s no new class, no subclassing – you simply attach to an event to implement an event handler method with a straight forward signature to retrieve the stream or string you’re interested in. The implementation is based on a subclass of Stream as is required in order to handle the Response.Filter requirements. What’s different than other implementations I’ve seen in various places is that it supports capturing output as a whole to allow retrieving the full response output for capture or modification. The exception are the TransformWrite and TransformWrite events which operate only active chunk of data written by the Response. For captured output, the Write() method captures output into an internal MemoryStream that is cached until writing is complete. So Write() is called when ASP.NET writes to the Response stream, but the filter doesn’t pass on the Write immediately to the filter’s internal stream. The data is cached and only when the Flush() method is called to finalize the Stream’s output do we actually send the cached stream off for transformation (if the events are hooked up) and THEN finally write out the returned content in one big chunk. Here’s the implementation of ResponseFilterStream: /// <summary> /// A semi-generic Stream implementation for Response.Filter with /// an event interface for handling Content transformations via /// Stream or String. /// <remarks> /// Use with care for large output as this implementation copies /// the output into a memory stream and so increases memory usage. /// </remarks> /// </summary> public class ResponseFilterStream : Stream { /// <summary> /// The original stream /// </summary> Stream _stream; /// <summary> /// Current position in the original stream /// </summary> long _position; /// <summary> /// Stream that original content is read into /// and then passed to TransformStream function /// </summary> MemoryStream _cacheStream = new MemoryStream(5000); /// <summary> /// Internal pointer that that keeps track of the size /// of the cacheStream /// </summary> int _cachePointer = 0; /// <summary> /// /// </summary> /// <param name="responseStream"></param> public ResponseFilterStream(Stream responseStream) { _stream = responseStream; } /// <summary> /// Determines whether the stream is captured /// </summary> private bool IsCaptured { get { if (CaptureStream != null || CaptureString != null || TransformStream != null || TransformString != null) return true; return false; } } /// <summary> /// Determines whether the Write method is outputting data immediately /// or delaying output until Flush() is fired. /// </summary> private bool IsOutputDelayed { get { if (TransformStream != null || TransformString != null) return true; return false; } } /// <summary> /// Event that captures Response output and makes it available /// as a MemoryStream instance. Output is captured but won't /// affect Response output. /// </summary> public event Action<MemoryStream> CaptureStream; /// <summary> /// Event that captures Response output and makes it available /// as a string. Output is captured but won't affect Response output. /// </summary> public event Action<string> CaptureString; /// <summary> /// Event that allows you transform the stream as each chunk of /// the output is written in the Write() operation of the stream. /// This means that that it's possible/likely that the input /// buffer will not contain the full response output but only /// one of potentially many chunks. /// /// This event is called as part of the filter stream's Write() /// operation. /// </summary> public event Func<byte[], byte[]> TransformWrite; /// <summary> /// Event that allows you to transform the response stream as /// each chunk of bytep[] output is written during the stream's write /// operation. This means it's possibly/likely that the string /// passed to the handler only contains a portion of the full /// output. Typical buffer chunks are around 16k a piece. /// /// This event is called as part of the stream's Write operation. /// </summary> public event Func<string, string> TransformWriteString; /// <summary> /// This event allows capturing and transformation of the entire /// output stream by caching all write operations and delaying final /// response output until Flush() is called on the stream. /// </summary> public event Func<MemoryStream, MemoryStream> TransformStream; /// <summary> /// Event that can be hooked up to handle Response.Filter /// Transformation. Passed a string that you can modify and /// return back as a return value. The modified content /// will become the final output. /// </summary> public event Func<string, string> TransformString; protected virtual void OnCaptureStream(MemoryStream ms) { if (CaptureStream != null) CaptureStream(ms); } private void OnCaptureStringInternal(MemoryStream ms) { if (CaptureString != null) { string content = HttpContext.Current.Response.ContentEncoding.GetString(ms.ToArray()); OnCaptureString(content); } } protected virtual void OnCaptureString(string output) { if (CaptureString != null) CaptureString(output); } protected virtual byte[] OnTransformWrite(byte[] buffer) { if (TransformWrite != null) return TransformWrite(buffer); return buffer; } private byte[] OnTransformWriteStringInternal(byte[] buffer) { Encoding encoding = HttpContext.Current.Response.ContentEncoding; string output = OnTransformWriteString(encoding.GetString(buffer)); return encoding.GetBytes(output); } private string OnTransformWriteString(string value) { if (TransformWriteString != null) return TransformWriteString(value); return value; } protected virtual MemoryStream OnTransformCompleteStream(MemoryStream ms) { if (TransformStream != null) return TransformStream(ms); return ms; } /// <summary> /// Allows transforming of strings /// /// Note this handler is internal and not meant to be overridden /// as the TransformString Event has to be hooked up in order /// for this handler to even fire to avoid the overhead of string /// conversion on every pass through. /// </summary> /// <param name="responseText"></param> /// <returns></returns> private string OnTransformCompleteString(string responseText) { if (TransformString != null) TransformString(responseText); return responseText; } /// <summary> /// Wrapper method form OnTransformString that handles /// stream to string and vice versa conversions /// </summary> /// <param name="ms"></param> /// <returns></returns> internal MemoryStream OnTransformCompleteStringInternal(MemoryStream ms) { if (TransformString == null) return ms; //string content = ms.GetAsString(); string content = HttpContext.Current.Response.ContentEncoding.GetString(ms.ToArray()); content = TransformString(content); byte[] buffer = HttpContext.Current.Response.ContentEncoding.GetBytes(content); ms = new MemoryStream(); ms.Write(buffer, 0, buffer.Length); //ms.WriteString(content); return ms; } /// <summary> /// /// </summary> public override bool CanRead { get { return true; } } public override bool CanSeek { get { return true; } } /// <summary> /// /// </summary> public override bool CanWrite { get { return true; } } /// <summary> /// /// </summary> public override long Length { get { return 0; } } /// <summary> /// /// </summary> public override long Position { get { return _position; } set { _position = value; } } /// <summary> /// /// </summary> /// <param name="offset"></param> /// <param name="direction"></param> /// <returns></returns> public override long Seek(long offset, System.IO.SeekOrigin direction) { return _stream.Seek(offset, direction); } /// <summary> /// /// </summary> /// <param name="length"></param> public override void SetLength(long length) { _stream.SetLength(length); } /// <summary> /// /// </summary> public override void Close() { _stream.Close(); } /// <summary> /// Override flush by writing out the cached stream data /// </summary> public override void Flush() { if (IsCaptured && _cacheStream.Length > 0) { // Check for transform implementations _cacheStream = OnTransformCompleteStream(_cacheStream); _cacheStream = OnTransformCompleteStringInternal(_cacheStream); OnCaptureStream(_cacheStream); OnCaptureStringInternal(_cacheStream); // write the stream back out if output was delayed if (IsOutputDelayed) _stream.Write(_cacheStream.ToArray(), 0, (int)_cacheStream.Length); // Clear the cache once we've written it out _cacheStream.SetLength(0); } // default flush behavior _stream.Flush(); } /// <summary> /// /// </summary> /// <param name="buffer"></param> /// <param name="offset"></param> /// <param name="count"></param> /// <returns></returns> public override int Read(byte[] buffer, int offset, int count) { return _stream.Read(buffer, offset, count); } /// <summary> /// Overriden to capture output written by ASP.NET and captured /// into a cached stream that is written out later when Flush() /// is called. /// </summary> /// <param name="buffer"></param> /// <param name="offset"></param> /// <param name="count"></param> public override void Write(byte[] buffer, int offset, int count) { if ( IsCaptured ) { // copy to holding buffer only - we'll write out later _cacheStream.Write(buffer, 0, count); _cachePointer += count; } // just transform this buffer if (TransformWrite != null) buffer = OnTransformWrite(buffer); if (TransformWriteString != null) buffer = OnTransformWriteStringInternal(buffer); if (!IsOutputDelayed) _stream.Write(buffer, offset, buffer.Length); } } The key features are the events and corresponding OnXXX methods that handle the event hookups, and the Write() and Flush() methods of the stream implementation. All the rest of the members tend to be plain jane passthrough stream implementation code without much consequence. I do love the way Action<t> and Func<T> make it so easy to create the event signatures for the various events – sweet. A few Things to consider Performance Response.Filter is not great for performance in general as it adds another layer of indirection to the ASP.NET output pipeline, and this implementation in particular adds a memory hit as it basically duplicates the response output into the cached memory stream which is necessary since you may have to look at the entire response. If you have large pages in particular this can cause potentially serious memory pressure in your server application. So be careful of wholesale adoption of this (or other) Response.Filters. Make sure to do some performance testing to ensure it’s not killing your app’s performance. Response.Filter works everywhere A few questions came up in comments and discussion as to capturing ALL output hitting the site and – yes you can definitely do that by assigning a Response.Filter inside of a module. If you do this however you’ll want to be very careful and decide which content you actually want to capture especially in IIS 7 which passes ALL content – including static images/CSS etc. through the ASP.NET pipeline. So it is important to filter only on what you’re looking for – like the page extension or maybe more effectively the Response.ContentType. Response.Filter Chaining Originally I thought that filter chaining doesn’t work at all due to a bug in the stream implementation code. But it’s quite possible to assign multiple filters to the Response.Filter property. So the following actually works to both compress the output and apply the transformed content: WebUtils.GZipEncodePage(); ResponseFilterStream filter = new ResponseFilterStream(Response.Filter); filter.TransformString += filter_TransformString; Response.Filter = filter; However the following does not work resulting in invalid content encoding errors: ResponseFilterStream filter = new ResponseFilterStream(Response.Filter); filter.TransformString += filter_TransformString; Response.Filter = filter; WebUtils.GZipEncodePage(); In other words multiple Response filters can work together but it depends entirely on the implementation whether they can be chained or in which order they can be chained. In this case running the GZip/Deflate stream filters apparently relies on the original content length of the output and chokes when the content is modified. But if attaching the compression first it works fine as unintuitive as that may seem. Resources Download example code Capture Output from ASP.NET Pages © Rick Strahl, West Wind Technologies, 2005-2010Posted in ASP.NET  

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  • Android stream to Wowza

    - by Curtis Kiu
    I feel very confused about Android streaming to wowza. I am doing a video conference using rtmp cross-platform, but Android doesn't eat RTMP. Therefore I need to find another way to do it. Upstreaming I found a new open-source app called spydroid-ipcamera. It is using rtp, sending udp packets to computer, and opens it in vlc using the following sdp v=0 s=Unnamed m=video 5006 RTP/AVP 96 a=rtpmap:96 H264/90000 a=fmtp:96 packetization-mode=1;profile-level-id=420016;sprop-parameter-sets=Z0IAFukBQHsg,aM4BDyA=; But it can't work. Then I follow wowza tutorial and stream to it and then play again in VLC. That works! I wrote it in http://code.google.com/p/spydroid-ipcamera/issues/detail?id=2 However when I want to add audio in the packet, it fails to work. I change to code in http://code.google.com/p/spydroid-ipcamera/source/browse/trunk/src/net/mkp/spydroid/CameraStreamer.java mr.setAudioSource(MediaRecorder.AudioSource.MIC); mr.setVideoSource(MediaRecorder.VideoSource.CAMERA); mr.setOutputFormat(MediaRecorder.OutputFormat.MPEG_4); mr.setVideoFrameRate(20); mr.setVideoSize(640, 480); mr.setAudioEncoder(MediaRecorder.AudioEncoder.AAC); mr.setVideoEncoder(MediaRecorder.VideoEncoder.H264); mr.setPreviewDisplay(holder.getSurface()); Then I thought that the problem should be in sdp, but I don't know how to due with sdp. I am streaming H.264/AAC with Mp4 Second I don't understand sdp. So how can I make video conference upstreaming part using this apps. Android ----(UDP Port:5006)----> PC (SDP file) and then Wowza read the SDP file ------> VLC I think in this way the system cannot handle more than 1 client. sdp can only hold 1 port, any idea or actually it wont' work? Also Wowza need to set the stream before we stream it, so does it mean that I should not follow this way to do it? Sorry my English is poor, I hope you guys understand.

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  • Stream a continously growing file over tcp/ip

    - by Grinner
    Hello, I have a project I'm working on, where a piece of Hardware is producing output that is continuously being written into a textfile. What I need to do is to stream that file as it's being written over a simple tcp/ip connection. I'm currently trying to that through simple netcat, but netcat only sends the part of the file that is written at the time of execution. It doesn't continue to send the rest. Right now I have a server listening to netcat on port 9000 (simply for test-purposes): netcat -l 9000 And the send command is: netcat localhost 9000 < c:\OUTPUTFILE So in my understanding netcat should actually be streaming the file, but it simply stops once everything that existed at the beginning of the execution has been sent. It doesn't kill the connection, but simply stops sending new data. How do I get it to stream the data continuously? Thanks for any help!

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  • Virtual camera/direct show filter for network stream

    - by Jeje
    Hi guys, i'm working with Flash Live Encoder. It's using camera for streaming video. Support forum say's that i can create custom direct show filter and stream data that i need. I cann't understand how direct show filter will display in the source list of the live encoder. I've tryed to use some commercial virtual camera and it work's fine, but it cann't use source from network stream. Summary. I have a several network streams. I think that i must to create virtual camera for each one. But if i find examples with direct show filter on C#, i cann't find for virtual camera.

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  • Search the public stream in Facebook

    - by camilo_u
    Hi, Is there any change i can search for anything in the Open Stream in Facebook? Let´s say that i want to look for "obama", this will return all of the obama mentions for a bunch of people in their streams, so far I haven't found anything like this, probably only looking in one user stream, but not the whole stuff. So, i haven't found a way to do this, but how come, sites like socialmention.com can do it? Do they query user by user streams? and how to do it without users permissions? What do you guys think? Thanks in advance! Camilo

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  • Play mp3 stream from http URL on Windows Mobile 6.0

    - by Thyphuong
    After a short period of time learning about how to play a mp3 http url on windows mobile 6.0, I found that very less dll support that (until now, I just found out Bass.dll work nice). So I intend to change to another way to approach the goal. Here's my idea: Get a stream from http url. Decode the mp3 stream. Play the result from step 2. Coz I'm new on this field, so feel free and explain to me what I'm wrong and/or show me the way.

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  • Problems with Facebook API - Getting all content from table Stream

    - by Fernando Paiva
    I am trying to get all stream data from a group (I have wall entries, discussions, events and photos). For now, Access on this group is Open. $result = $_fb-api_client-fql_query("SELECT actor_id, message FROM stream WHERE source_id=$gid LIMIT 50"); Only some of the records come back (5 out of 10) (only wall entries and a photo). Just in case, I asked for extra permission when user signed up for the app (just to make sure is not a lack of permissions - even though the Group is "open" right now): Access my News Feed & Wall Send SMS messages to my phone Create and modify events RSVP to events Access my data when I'm not using the application Publish content to my Wall Access my email address Access Insights data for my pages and applications

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  • Issue with SQL query for activity stream/feed

    - by blabus
    I'm building an application that allows users to recommend music to each other, and am having trouble building a query that would return a 'stream' of recommendations that involve both the user themselves, as well as any of the user's friends. This is my table structure: Recommendations ID Sender Recipient [other columns...] -- ------ --------- ------------------ r1 u1 u3 ... r2 u3 u2 ... r3 u4 u3 ... Users ID Email First Name Last Name [other columns...] --- ----- ---------- --------- ------------------ u1 ... ... ... ... u2 ... ... ... ... u3 ... ... ... ... u4 ... ... ... ... Relationships ID Sender Recipient Status [other columns...] --- ------ --------- -------- ------------------ rl1 u1 u2 accepted ... rl2 u3 u1 accepted ... rl3 u1 u4 accepted ... rl4 u3 u2 accepted ... So for user 'u4' (who is friends with 'u1'), I want to query for a 'stream' of recommendations relevant to u4. This stream would include all recommendations in which either the sender or recipient is u4, as well as all recommendations in which the sender or recipient is u1 (the friend). This is what I have for the query so far: SELECT * FROM recommendations WHERE recommendations.sender IN ( SELECT sender FROM relationships WHERE recipient='u4' AND status='accepted' UNION SELECT recipient FROM relationships WHERE sender='u4' AND status='accepted') OR recommendations.recipient IN ( SELECT sender FROM relationships WHERE recipient='u4' AND status='accepted' UNION SELECT recipient FROM relationships WHERE sender='u4' AND status='accepted') UNION SELECT * FROM recommendations WHERE recommendations.sender='u4' OR recommendations.recipient='u4' GROUP BY recommendations.id ORDER BY datecreated DESC Which seems to work, as far as I can see (I'm no SQL expert). It returns all of the records from the Recommendations table that would be 'relevant' to a given user. However, I'm now having trouble also getting data from the Users table as well. The Recommendations table has the sender's and recipient's ID (foreign keys), but I'd also like to get the first and last name of each as well. I think I require some sort of JOIN, but I'm lost on how to proceed, and was looking for help on that. (And also, if anyone sees any areas for improvement in my current query, I'm all ears.) Thanks!

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