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  • Big Data – Operational Databases Supporting Big Data – Key-Value Pair Databases and Document Databases – Day 13 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned the importance of the Relational Database and NoSQL database in the Big Data Story. In this article we will understand the role of Key-Value Pair Databases and Document Databases Supporting Big Data Story. Now we will see a few of the examples of the operational databases. Relational Databases (Yesterday’s post) NoSQL Databases (Yesterday’s post) Key-Value Pair Databases (This post) Document Databases (This post) Columnar Databases (Tomorrow’s post) Graph Databases (Tomorrow’s post) Spatial Databases (Tomorrow’s post) Key Value Pair Databases Key Value Pair Databases are also known as KVP databases. A key is a field name and attribute, an identifier. The content of that field is its value, the data that is being identified and stored. They have a very simple implementation of NoSQL database concepts. They do not have schema hence they are very flexible as well as scalable. The disadvantages of Key Value Pair (KVP) database are that they do not follow ACID (Atomicity, Consistency, Isolation, Durability) properties. Additionally, it will require data architects to plan for data placement, replication as well as high availability. In KVP databases the data is stored as strings. Here is a simple example of how Key Value Database will look like: Key Value Name Pinal Dave Color Blue Twitter @pinaldave Name Nupur Dave Movie The Hero As the number of users grow in Key Value Pair databases it starts getting difficult to manage the entire database. As there is no specific schema or rules associated with the database, there are chances that database grows exponentially as well. It is very crucial to select the right Key Value Pair Database which offers an additional set of tools to manage the data and provides finer control over various business aspects of the same. Riak Rick is one of the most popular Key Value Database. It is known for its scalability and performance in high volume and velocity database. Additionally, it implements a mechanism for collection key and values which further helps to build manageable system. We will further discuss Riak in future blog posts. Key Value Databases are a good choice for social media, communities, caching layers for connecting other databases. In simpler words, whenever we required flexibility of the data storage keeping scalability in mind – KVP databases are good options to consider. Document Database There are two different kinds of document databases. 1) Full document Content (web pages, word docs etc) and 2) Storing Document Components for storage. The second types of the document database we are talking about over here. They use Javascript Object Notation (JSON) and Binary JSON for the structure of the documents. JSON is very easy to understand language and it is very easy to write for applications. There are two major structures of JSON used for Document Database – 1) Name Value Pairs and 2) Ordered List. MongoDB and CouchDB are two of the most popular Open Source NonRelational Document Database. MongoDB MongoDB databases are called collections. Each collection is build of documents and each document is composed of fields. MongoDB collections can be indexed for optimal performance. MongoDB ecosystem is highly available, supports query services as well as MapReduce. It is often used in high volume content management system. CouchDB CouchDB databases are composed of documents which consists fields and attachments (known as description). It supports ACID properties. The main attraction points of CouchDB are that it will continue to operate even though network connectivity is sketchy. Due to this nature CouchDB prefers local data storage. Document Database is a good choice of the database when users have to generate dynamic reports from elements which are changing very frequently. A good example of document usages is in real time analytics in social networking or content management system. Tomorrow In tomorrow’s blog post we will discuss about various other Operational Databases supporting 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 reader returned by the store data provider does not have enough columns

    - by molgan
    Hello I get the following error when I try to execute a stored procedure: "The data reader returned by the store data provider does not have enough columns" When I in the sql-manager execute it like this: DECLARE @return_value int, @EndDate datetime EXEC @return_value = [dbo].[GetSomeDate] @SomeID = 91, @EndDate = @EndDate OUTPUT SELECT @EndDate as N'@EndDate' SELECT 'Return Value' = @return_value GO It returns the value properly.... @SomeDate = '2010-03-24 09:00' And in my app I have: if (_entities.Connection.State == System.Data.ConnectionState.Closed) _entities.Connection.Open(); using (EntityCommand c = new EntityCommand("MyAppEntities.GetSomeDate", (EntityConnection)this._entities.Connection)) { c.CommandType = System.Data.CommandType.StoredProcedure; EntityParameter paramSomeID = new EntityParameter("SomeID", System.Data.DbType.Int32); paramSomeID.Direction = System.Data.ParameterDirection.Input; paramSomeID.Value = someID; c.Parameters.Add(paramSomeID); EntityParameter paramSomeDate = new EntityParameter("SomeDate", System.Data.DbType.DateTime); SomeDate.Direction = System.Data.ParameterDirection.Output; c.Parameters.Add(paramSomeDate); int retval = c.ExecuteNonQuery(); return (DateTime?)c.Parameters["SomeDate"].Value; Why does it complain about columns? I googled on error and someone said something about removing RETURN in sp, but I dont have any RETURN there. last like is like SELECT @SomeDate = D.SomeDate FROM .... /M

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  • ASP.NET server data persistence

    - by Wayne Werner
    Hi, I'm not really sure exactly how the question should be phrased, so please be patient if I ask the wrong thing. I'm writing an ASP.NET application using VB as the code behind language. I have a data access class that connects to the DB to run the query (parameterized, of course), and another class to perform the validation tasks - I access this class from my aspx page. What I would like is to be able to store the data server side and wait for the user to choose from a few options based on the validity of the data. But unless my understanding is completely off, having persistent data objects on the server will give problems when multiple users connect? My ultimate goal is that once the data has been validated the end user can't modify it. Currently I'm validating the data, but I still have to retrieve it from the web form AFTER the user says OK, which obviously leaves open the possibility of injecting bad data either accidentally (unlikely) or on purpose (also unlikely for the use, but I'd prefer not to take the chance). So am I completely off in my understanding? If so, can someone point me to a resource that provides some instructions on keeping persistent data on the server, or provide instruction? Thanks!

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  • SQLAuthority News – Best Practices for Data Warehousing with SQL Server 2008 R2

    - by pinaldave
    An integral part of any BI system is the data warehouse—a central repository of data that is regularly refreshed from the source systems. The new data is transferred at regular intervals  by extract, transform, and load (ETL) processes. This whitepaper talks about what are best practices for Data Warehousing. This whitepaper discusses ETL, Analysis, Reporting as well relational database. The main focus of this whitepaper is on mainly ‘architecture’ and ‘performance’. Download Best Practices for Data Warehousing with SQL Server 2008 R2 Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Best Practices, Data Warehousing, PostADay, SQL, SQL Authority, SQL Documentation, SQL Download, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Nagy dobás készül az Oracle adatányászati felületen, Oracle Data Mining

    - by Fekete Zoltán
    Ahogyan már a tavaly oszi Oracle OpenWorld hírekben és eloadásokban is láthattuk a beharangozót, az Oracle nagy dobásra készül az adatbányászati fronton (Oracle Data Mining), mégpedig a remekül használható adatbányászati motor grafikus felületének a kiterjesztésével. Ha jól megfigyeljük ezt az utóbbi linket, az eddigi grafikus felület már Oracle Data Miner Classic néven fut. Hogyan is lehet használni az Oracle Data Mining-ot? - Oracle Data Miner (ingyenesen letöltheto GUI az OTN-rol) - Java-ból és PL/SQL-bol, Oracle Data Mining JDeveloper and SQL Developer Extensions - Excel felületrol, Oracle Spreadsheet Add-In for Predictive Analytics - ODM Connector for mySAP BW Oracle Data Mining technikai információ.

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  • Google I/O 2012 - Big Data: Turning Your Data Problem Into a Competitive Advantage

    Google I/O 2012 - Big Data: Turning Your Data Problem Into a Competitive Advantage Ju-kay Kwek, Navneet Joneja Can businesses get practical value from web-scale data without building proprietary web-scale infrastructure? This session will explore how new Google data services can be used to solve key data storage, transformation and analysis challenges. We will look at concrete case studies demonstrating how real life businesses have successfully used these solutions to turn data into a competitive business asset. For all I/O 2012 sessions, go to developers.google.com From: GoogleDevelopers Views: 1 0 ratings Time: 52:39 More in Science & Technology

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  • Data-Driven SOA with Oracle Data Integrator

    - by Irem Radzik
    v\:* {behavior:url(#default#VML);} o\:* {behavior:url(#default#VML);} w\:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);} Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Cambria","serif"; mso-fareast-font-family:"MS Mincho";} By Mike Eisterer, Data integration is more than simply moving data in bulk or in real-time, it is also about unifying information for improved business agility and integrating it in today’s service-oriented architectures. SOA enables organizations to easily define services which may then be discovered and leveraged by varying consumers. These consumers may be applications, customer facing portals, or complex business rules which are assembling services to automate process. Data as a foundational service provider is a key component of today’s successful SOA implementations. Oracle offers the broadest and most integrated portfolio of products to help you define, organize, orchestrate and consume data services. If you are attending Oracle OpenWorld next week, you will have ample opportunity to see the latest Oracle Data Integrator live in action and work with it yourself in two offered Hands-on Labs. Visit the hands-on lab to gain experience firsthand: Oracle Data Integrator and Oracle SOA Suite: Hands-on- Lab (HOL10480) Wed Oct 3rd 11:45AM Marriott Marquis- Salon 1/2 To learn more about Oracle Data Integrator, please visit our Introduction Hands-on LAB: Introduction to Oracle Data Integrator (HOL10481) Mon Oct 1st 3:15PM, Marriott Marquis- Salon 1/2 If you are not able to attend OpenWorld, please check out our latest resources for Data Integration.

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  • Subsetting a data frame in a function using another data frame as parameter

    - by lecodesportif
    I would like to submit a data frame to a function and use it to subset another data frame. This is the basic data frame: foo <- data.frame(var1= c('1', '1', '1', '2', '2', '3'), var2=c('A', 'A', 'B', 'B', 'C', 'C')) I use the following function to find out the frequencies of var2 for specified values of var1. foobar <- function(x, y, z){ a <- subset(x, (x$var1 == y)) b <- subset(a, (a$var2 == z)) n=nrow(b) return(n) } Examples: foobar(foo, 1, "A") # returns 2 foobar(foo, 1, "B") # returns 1 foobar(foo, 3, "C") # returns 1 This works. But now I want to submit a data frame of values to foobar. Instead of the above examples, I would like to submit df to foobar and get the same results as above (2, 1, 1) df <- data.frame(var1=c('1','1','3'), var2=c("A", "B", "C")) When I change foobar to accept two arguments like foobar(foo, df) and use y[, c(var1)] and y[, c(var2)] instead of the two parameters x and y it still doesn't work. Which way is there to do this?

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  • Managing Data Dependecies of Java Classes that Load Data from the Classpath at Runtime

    - by Martin Potthast
    What is the simplest way to manage dependencies of Java classes to data files present in the classpath? More specifically: How should data dependencies be annotated? Perhaps using Java annotations (e.g., @Data)? Or rather some build entries in a build script or a properties file? Is there build tool that integrates and evaluates such information (Ant, Scons, ...)? Do you have examples? Consider the following scenario: A few lines of Ant create a Jar from my sources that includes everything found on the classpath. Then jarjar is used to remove all .class files that are not necessary to execute, say, class Foo. The problem is that all the data files that class Bar depends upon are still there in the Jar. The ideal deployment script, however, would recognize that the data files on which only class Bar depends can be removed while data files on which class Foo depends must be retained. Any hints?

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  • Best way to implement user-powered data validation

    - by vegetables
    I run a product recommendation engine and I'm hitting a few snags. I'm looking to see if anyone has any recommendations on what I should do to minimize these issues. Here's how the site works: Users come to the site and are presented with product recommendations based on some criteria. If a user knows of a product that is not in our system, they can add it by providing the product name and manufacturer. We take that information, and: Hit one API to gather all the product meta-data (and to validate the product spelling, etc). If the product is not in this first API, we do not allow it in our system. Use the information from step 1 to hit another API for pricing information (gathered from many places online). For the sake of discussion, assume that I am searching both APIs in the most efficient/successful manner possible. For the most part, this works very well. I'd say ~80% of our data is perfectly accurate, but there are a few issues: Sometimes the pricing API (Step 2) doesn't have any information for the product. The way the pricing API is built, it will always return something (theoretically, the closest possible match), and there's no guarantee that the product name is spelled exactly the same way in both APIs, so there's no automated way of knowing if it's the right product. When the pricing API finds the right product, occasionally it has outdated, or even invalid pricing data (e.g. if it screen-scraped the wrong price from a website). Since the site was fairly small at first, I was able to manually verify every product that was added to the website. However, the site has grown to the point where this is taking several hours per day, and is just not efficient use of my time. So, my question is: Aside from hiring someone (or getting an intern) to validate all the data manually, what would be the best system of letting my userbase self-manage the data. Specifically, how can I allow users to edit the data while minimizing the risk of someone ambushing my website, or accidentally setting the data incorrectly.

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  • data handling with javascript

    - by Vincent Warmerdam
    Python has a very neat package called pandas which allows for quick data transformation; tables, aggregation, that sort of thing. A lot of these types of functionality can also be found in the python itertools module. The plyR package in R is also very similar. Usually one woud use this functionality to produce a table which is later visualized with a plot. I am personally very fond of d3, and I would like to allow the user to first indicate what type of data aggregation he wants on the dataset before it is visualized. The visualisation in question involves making a heatmap where the user gets to select the size of the bins of the heatmap beforehand (I want d3 to project this through leaflet). I want to visually select the ideal size of the bins for the heatmap. The way I work now is that I take the dataset, aggregate it with python and then manually load it in d3. This is a process that takes a lot of human effort and I was wondering if the data aggregation can be done through the javascript of the browser. I couldn't find a package for javascript specifically built for data, suggesting (to me) that this is a bad idea and that one should not use javascript for the data handling. Is there a good module/package for javascript to handle data aggregation? Is it a good/bad idea to do the data aggregation in javascript (performance wise)?

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  • Customizing the NUnit GUI for data-driven testing

    - by rwong
    My test project consists of a set of input data files which is fed into a piece of legacy third-party software. Since the input data files for this software are difficult to construct (not something that can be done intentionally), I am not going to add new input data files. Each input data file will be subject to a set of "test functions". Some of the test functions can be invoked independently. Other test functions represent the stages of a sequential operation - if an earlier stage fails, the subsequent stages do not need to be executed. I have experimented with the NUnit parametrized test case (TestCaseAttribute and TestCaseSourceAttribute), passing in the list of data files as test cases. I am generally satisfied with the the ability to select the input data for testing. However, I would like to see if it is possible to customize its GUI's tree structure, so that the "test functions" become the children of the "input data". For example: File #1 CheckFileTypeTest GetFileTopLevelStructureTest CompleteProcessTest StageOneTest StageTwoTest StageThreeTest File #2 CheckFileTypeTest GetFileTopLevelStructureTest CompleteProcessTest StageOneTest StageTwoTest StageThreeTest This will be useful for identifying the stage that failed during the processing of a particular input file. Is there any tips and tricks that will enable the new tree layout? Do I need to customize NUnit to get this layout?

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  • Test Data in a Distributed System

    - by Davin Tryon
    A question that has been vexing me lately has been about how to effectively test (end-to-end) features in a distributed system. Particuarly, how to effectively manage (through time) test data for feature testing. The system in question is a typical SOA setup. The composition is done in JavaScript when call to several REST APIs. Each service is built as an independent block. Each service has some kind of persistent storage (SQL Server in most cases). The main issue at the moment is how to approach test data when testing end-to-end features. Functional end-to-end testing occurs through the UI, and it is therefore necessary for test data to be set up before the test run (this could be manual or automated testing). As is typical in a distributed system, identifiers from one service are used as a link in another service. So, some level of synchronization needs to be present in the data to effectively test. What is the best way to manage and set up this data after a successful deployment to a test environment? For example, is it better to manage this test data inside each service? Or package it together with the testing suite? Does that testing suite exist as a separate project? I'm interested in design guidance about how to store and manage this test data as the application features evolve.

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  • Data Management Business Continuity Planning

    Business Continuity Governance In order to ensure data continuity for an organization, they need to ensure they know how to handle a data or network emergency because all systems have the potential to fail. Data Continuity Checklist: Disaster Recovery Plan/Policy Backups Redundancy Trained Staff Business Continuity Policies In order to protect data in case of any emergency a company needs to put in place a Disaster recovery plan and policies that can be executed by IT staff to ensure the continuity of the existing data and/or limit the amount of data that is not contiguous.  A disaster recovery plan is a comprehensive statement of consistent actions to be taken before, during and after a disaster, according to Geoffrey H. Wold. He also states that the primary objective of disaster recovery planning is to protect the organization in the event that all or parts of its operations and/or computer services are rendered unusable. Furthermore, companies can mandate through policies that IT must maintain redundant hardware in case of any hardware failures and redundant network connectivity incase the primary internet service provider goes down.  Additionally, they can require that all staff be trained in regards to the Disaster recovery policy to ensure that all parties evolved are knowledgeable to execute the recovery plan. Business Continuity Procedures Business continuity procedure vary from organization to origination, however there are standard procedures that most originations should follow. Standard Business Continuity Procedures Backup and Test Backups to ensure that they work Hire knowledgeable and trainable staff  Offer training on new and existing systems Regularly monitor, test, maintain, and upgrade existing system hardware and applications Maintain redundancy regarding all data, and critical business functionality

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  • How to choose how to store data?

    - by Eldros
    Give a man a fish and you feed him for a day. Teach a man to fish and you feed him for a lifetime. - Chinese Proverb I could ask what kind of data storage I should use for my actual project, but I want to learn to fish, so I don't need to ask for a fish each time I begin a new project. So, until I used two methods to store data on my non-game project: XML files, and relational databases. I know that there is also other kind of database, of the NoSQL kind. However I wouldn't know if there is more choice available to me, or how to choose in the first place, aside arbitrary picking one. So the question is the following: How should I choose the kind of data storage for a game project? And I would be interested on the following criterion when choosing: The size of the project. The platform targeted by the game. The complexity of the data structure. Added Portability of data amongst many project. Added How often should the data be accessed Added Multiple type of data for a same application Any other point you think is of interest when deciding what to use. EDIT I know about Would it be better to use XML/JSON/Text or a database to store game content?, but thought it didn't address exactly my point. Now if I am wrong, I would gladely be shown the error in my ways.

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  • Data Masking Pack 12.1.0.3 Certified with E-Business Suite 12.1.3

    - by Elke Phelps (Oracle Development)
    I'm pleased to announce the certification of the E-Business Suite 12.1.3 Data Masking Template for the Data Masking Pack with Enterprise Manager Cloud Control 12.1.0.3. You can use the Oracle Data Masking Pack with Oracle Enterprise Manager Grid Control 12c to scramble sensitive data in cloned E-Business Suite environments.     You may scramble data in E-Business Suite cloned environments with EM12.1.0.3 using the following template: E-Business Suite 12.1.3 Data Masking Template for Data Masking Pack with EM12c (Patch 18462641) What does data masking do in E-Business Suite environments? Application data masking does the following: De-identify the data:  Scramble identifiers of individuals, also known as personally identifiable information or PII.  Examples include information such as name, account, address, location, and driver's license number. Mask sensitive data:  Mask data that, if associated with personally identifiable information (PII), would cause privacy concerns.  Examples include compensation, health and employment information.   Maintain data validity:  Provide a fully functional application.  How can EBS customers use data masking? The Oracle E-Business Suite Template for Data Masking Pack can be used in situations where confidential or regulated data needs to be shared with other non-production users who need access to some of the original data, but not necessarily every table.  Examples of non-production users include internal application developers or external business partners such as offshore testing companies, suppliers or customers.  Due to data dependencies, scrambling E-Business Suite data is not a trivial task.  The data needs to be scrubbed in such a way that allows the application to continue to function. The template works with the Oracle Data Masking Pack and Oracle Enterprise Manager to obscure sensitive E-Business Suite information that is copied from production to non-production environments.  The Oracle E-Business Suite Template for Data Masking Pack is applied to a non-production environment with the Enterprise Manager Grid Control Data Masking Pack.  When applied, the Oracle E-Business Suite Template for Data Masking Pack will create an irreversibly scrambled version of your production database for development and testing. Is there a charge for this? Yes. You must purchase licenses for the Oracle Data Masking Pack to use the Oracle E-Business Suite 12.1.3 template. The Oracle E-Business Suite 12.1.3 Template for the Data Masking Pack is included with the Oracle Data Masking Pack license.  You can contact your Oracle account manager for more details about licensing. References Additional details and requirements are provided in the following My Oracle Support Note: Using Oracle E-Business Suite Release 12.1.3 Template for the Data Masking Pack with Oracle Enterprise Manager 12.1 Data Masking Tool (Note 1481916.1) Masking Sensitive Data in the Oracle Database Real Application Testing User's Guide 11g Release 2 (11.2) Related Articles Scrambling Sensitive Data in E-Business Suite E-Business Suite 12.1.3 Data Masking Certified with Enterprise Manager 12c

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  • Oracle Data Warehouse and Big Data Magazine MAY Edition for Customers + Partners

    - by KLaker
    Follow us on The latest edition of our monthly data warehouse and big data magazine for Oracle customers and partners is now available. The content for this magazine is taken from the various data warehouse and big data Oracle product management blogs, Oracle press releases, videos posted on Oracle Media Network and Oracle Facebook pages. Click here to view the May Edition Please share this link http://flip.it/fKOUS to our magazine with your customers and partners This magazine is optimized for display on tablets and smartphones using the Flipboard App which is available from the Apple App store and Google Play store

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  • Version control and data provenance in charts, slides, and marketing materials that derive from code ouput

    - by EMS
    I develop as part of a small team that mostly does research and statistics stuff. But from the output of our code, other teams often create promotional materials, slides, presentations, etc. We run into a big problem because the marketing team (non-programmers) tend to use Excel, Adobe products, or other tools to carry out their work, and just want easy-to-use data formats from us. This leads to data provenance problems. We see email chains with attachments from 6 months ago and someone is saying "Hey, who generated this data. Can you generate more of it with the recent 6 months of results added in?" I want to help the other teams effectively use version control (my team uses it reasonably well for the code, but every other team classically comes up with many excuses to avoid it). For version controlling a software project where the participants are coders, I have some reasonable understanding of best practices and what to do. But for getting a team of marketing professionals to version control marketing materials and associate metadata about the software used to generate the data for the charts, I'm a bit at a loss. Some of the goals I'd like to achieve: Data that supported a material should never be associated with a person. As in, it should never be the case that someone says "Hey Person XYZ, I see you sent me this data as an attachment 6 months ago, can you update it for me?" Rather, data should be associated with the code and code-version of any code that was used to get it, and perhaps a team of many people who may maintain that code. Then references for data updates are about executing a specific piece of code, with a known version number. I'd like this to be a process that works easily with the tech that the marketing team already uses (e.g. Excel files, Adobe file, whatever). I don't want to burden them with needing to learn a bunch of new stuff just to use version control. They are capable folks, so learning something is fine. Ideally they could use our existing version control framework, but there are some issues around that. I think knowing some general best practices will be enough though, and I can handle patching that into the way our stuff works now. Are there any goals I am failing to think about? What are the time-tested ways to do something like this?

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  • What does it mean to treat data as an asset?

    What does it mean to treat data as an asset? When considering this concept, we must define what data is and how it can be considered an asset. Data can easily be defined as a collection of stored truths that are open to interpretation and manipulation.  Expanding on this definition, data can be viewed as a set of captured facts, measurements, and ideas used to make decisions. Furthermore, InvestorsWords.com defines asset as any item of economic value owned by an individual or corporation. Now let’s apply this definition of asset to our definition of data, and ask the following question. Can facts, measurements and ideas be items that are of economic value owned by an individual or corporation? The obvious answer is yes; data can be bought and sold like commodities or analyzed to make smarter business decisions.  We can look at the economic value of data in one of two ways. First, data can be sold as a commodity that can take the form of goods like eBooks, Training, Music, Movies, and so on. Customers are willing to pay to gain access to this data for their consumption. This directly implies that there is an economic value for data in the form of a commodity because customers see a value in obtaining it.  Secondly data can be used in making smarter business decisions that allow for companies to become more profitable and/or reduce their potential for risk in regards to how they operate.  In the past I have worked at companies where we had to analyze previous sales activities in conjunction with current activities to determine how the company was preforming for the quarter.  In addition trends can be formulated based on existing data that allow companies to forecast data so that they can make strategic business decisions based sound forecasted data. Companies that truly value their data are constantly trying to grow and upgrade their data and supporting applications because it is the life blood of a company. If we look at an eBook retailer for example, imagine if they lost all of their data. They would be in essence forced out of business because they would have nothing to sell. In turn, if we look at a company that was using data to facilitate better decision making processes and they lost all of their data then they could be losing potential revenue and/ or increasing the company’s losses by making important business decisions virtually in the dark compared to when they were made on solid data.

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  • Data Virtualization: Federated and Hybrid

    - by Krishnamoorthy
    Data becomes useful when it can be leveraged at the right time. Not only enterprises application stores operate on large volume, velocity and variety of data. Mobile and social computing are in the need of operating in foresaid data. Replicating and transferring large swaths of data is one challenge faced in the field of data integration. However, smaller chunks of data aggregated from a variety of sources presents and even more interesting challenge in the industry. Over the past few decades, technology trends focused on best user experience, operating systems, high performance computing, high performance web sites, analysis of warehouse data, service oriented architecture, social computing, cloud computing, and big data. Operating on the ‘dark data’ becomes mandatory in the future technology trend, although, no solution can make dark data useful data in a single day. Useful data can be quantified by the facts of contextual, personalized and on time delivery. In most cases, data from a single source may not be complete the picture. Data has to be combined and computed from various sources, where data may be captured as hybrid data, meaning the combination of structured and unstructured data. Since related data is often found across disparate sources, effectively integrating these sources determines how useful this data ultimately becomes. Technology trends in 2013 are expected to focus on big data and private cloud. Consumers are not merely interested in where data is located or how data is retrieved and computed. Consumers are interested in how quick and how the data can be leveraged. In many cases, data virtualization is the right solution, and is expected to play a foundational role for SOA, Cloud integration, and Big Data. The Oracle Data Integration portfolio includes a data virtualization product called ODSI (Oracle Data Service Integrator). Unlike other data virtualization solutions, ODSI can perform both read and write operations on federated/hybrid data (RDBMS, Webservices,  delimited file and XML). The ODSI Engine is built on XQuery, hence ODSI user can perform computations on data either using XQuery or SQL. Built in data and query caching features, which reduces latency in repetitive calls. Rightly positioning ODSI, can results in a highly scalable model, reducing spend on additional hardware infrastructure.

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  • Architecture for a template-building, WYSIWIG application

    - by Sam Selikoff
    I'm building a WYSIWYG designer in Ember.js. The designer will allow users to create campaigns - think MailChimp. To build a campaign, users will choose an existing template. The template will have a defined layout. The user will then be taken to the designer, where he will be able to edit the text and style, and additionally change some layout options. I've been thinking about how best to go about structuring this app, and there are a few hurdles. Specifically, the output of the campaign will be dynamic: eventually, it will be published somewhere, and when the consumers (not my users, but the people clicking on the campaign that my user created) visit the campaign, certain pieces of data will change, depending on the type of consumer viewing the campaign. That means the ultimate output of the designer will be a dynamic site. The data that is dynamic for this site - the end product - will not be manipulated by the user in the designer. However, the data that will be manipulated by the user in the designer are things like copy, styles, layout options, etc. I'll call the first set of variables server-side data, and the second client-side data. It seems, then, that the process will go something like this: I'll need to create templates for this designer that have two dynamic segments. For instance, the server-side data could be Liquid expressions, and the client-side data Handlebars expressions. When the user creates a campaign, I would compile the template on the back end using some dummy data for the server-side variables, and serve up a handlebars template to the Ember app. The user would then edit the template, and the Ember app would save all his edits to the JS variables that were powering the template. This way he'd be able to preview the template. When he saves, he'll send back the selected template, along with all the data and options he's made. When it comes time to publish, the back-end system will have to do two things: compile the template with Handlebars using the campaign data, and then compile the template with Liquid using the server-side data Is my thinking roughly accurate about this, or is there a simpler way?

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  • Python what's the data structure for triple data

    - by Paul
    I've got a set of data that has three attributes, say A, B, and C, where A is kind of the index (i.e., A is used to look up the other two attributes.) What would be the best data structure for such data? I used two dictionaries, with A as the index of each. However, there's key errors when the query to the data doesn't match any instance of A.

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  • Implementing a generic repository for WCF data services

    - by cibrax
    The repository implementation I am going to discuss here is not exactly what someone would call repository in terms of DDD, but it is an abstraction layer that becomes handy at the moment of unit testing the code around this repository. In other words, you can easily create a mock to replace the real repository implementation. The WCF Data Services update for .NET 3.5 introduced a nice feature to support two way data bindings, which is very helpful for developing WPF or Silverlight based application but also for implementing the repository I am going to talk about. As part of this feature, the WCF Data Services Client library introduced a new collection DataServiceCollection<T> that implements INotifyPropertyChanged to notify the data context (DataServiceContext) about any change in the association links. This means that it is not longer necessary to manually set or remove the links in the data context when an item is added or removed from a collection. Before having this new collection, you basically used the following code to add a new item to a collection. Order order = new Order {   Name = "Foo" }; OrderItem item = new OrderItem {   Name = "bar",   UnitPrice = 10,   Qty = 1 }; var context = new OrderContext(); context.AddToOrders(order); context.AddToOrderItems(item); context.SetLink(item, "Order", order); context.SaveChanges(); Now, thanks to this new collection, everything is much simpler and similar to what you have in other ORMs like Entity Framework or L2S. Order order = new Order {   Name = "Foo" }; OrderItem item = new OrderItem {   Name = "bar",   UnitPrice = 10,   Qty = 1 }; order.Items.Add(item); var context = new OrderContext(); context.AddToOrders(order); context.SaveChanges(); In order to use this new feature, you first need to enable V2 in the data service, and then use some specific arguments in the datasvcutil tool (You can find more information about this new feature and how to use it in this post). DataSvcUtil /uri:"http://localhost:3655/MyDataService.svc/" /out:Reference.cs /dataservicecollection /version:2.0 Once you use those two arguments, the generated proxy classes will use DataServiceCollection<T> rather than a simple ObjectCollection<T>, which was the default collection in V1. There are some aspects that you need to know to use this feature correctly. 1. All the entities retrieved directly from the data context with a query track the changes and report those to the data context automatically. 2. A entity created with “new” does not track any change in the properties or associations. In order to enable change tracking in this entity, you need to do the following trick. public Order CreateOrder() {   var collection = new DataServiceCollection<Order>(this.context);   var order = new Order();   collection.Add(order);   return order; } You basically need to create a collection, and add the entity to that collection with the “Add” method to enable change tracking on that entity. 3. If you need to attach an existing entity (For example, if you created the entity with the “new” operator rather than retrieving it from the data context with a query) to a data context for tracking changes, you can use the “Load” method in the DataServiceCollection. var order = new Order {   Id = 1 }; var collection = new DataServiceCollection<Order>(this.context); collection.Load(order); In this case, the order with Id = 1 must exist on the data source exposed by the Data service. Otherwise, you will get an error because the entity did not exist. These cool extensions methods discussed by Stuart Leeks in this post to replace all the magic strings in the “Expand” operation with Expression Trees represent another feature I am going to use to implement this generic repository. Thanks to these extension methods, you could replace the following query with magic strings by a piece of code that only uses expressions. Magic strings, var customers = dataContext.Customers .Expand("Orders")         .Expand("Orders/Items") Expressions, var customers = dataContext.Customers .Expand(c => c.Orders.SubExpand(o => o.Items)) That query basically returns all the customers with their orders and order items. Ok, now that we have the automatic change tracking support and the expression support for explicitly loading entity associations, we are ready to create the repository. The interface for this repository looks like this,public interface IRepository { T Create<T>() where T : new(); void Update<T>(T entity); void Delete<T>(T entity); IQueryable<T> RetrieveAll<T>(params Expression<Func<T, object>>[] eagerProperties); IQueryable<T> Retrieve<T>(Expression<Func<T, bool>> predicate, params Expression<Func<T, object>>[] eagerProperties); void Attach<T>(T entity); void SaveChanges(); } The Retrieve and RetrieveAll methods are used to execute queries against the data service context. While both methods receive an array of expressions to load associations explicitly, only the Retrieve method receives a predicate representing the “where” clause. The following code represents the final implementation of this repository.public class DataServiceRepository: IRepository { ResourceRepositoryContext context; public DataServiceRepository() : this (new DataServiceContext()) { } public DataServiceRepository(DataServiceContext context) { this.context = context; } private static string ResolveEntitySet(Type type) { var entitySetAttribute = (EntitySetAttribute)type.GetCustomAttributes(typeof(EntitySetAttribute), true).FirstOrDefault(); if (entitySetAttribute != null) return entitySetAttribute.EntitySet; return null; } public T Create<T>() where T : new() { var collection = new DataServiceCollection<T>(this.context); var entity = new T(); collection.Add(entity); return entity; } public void Update<T>(T entity) { this.context.UpdateObject(entity); } public void Delete<T>(T entity) { this.context.DeleteObject(entity); } public void Attach<T>(T entity) { var collection = new DataServiceCollection<T>(this.context); collection.Load(entity); } public IQueryable<T> Retrieve<T>(Expression<Func<T, bool>> predicate, params Expression<Func<T, object>>[] eagerProperties) { var entitySet = ResolveEntitySet(typeof(T)); var query = context.CreateQuery<T>(entitySet); foreach (var e in eagerProperties) { query = query.Expand(e); } return query.Where(predicate); } public IQueryable<T> RetrieveAll<T>(params Expression<Func<T, object>>[] eagerProperties) { var entitySet = ResolveEntitySet(typeof(T)); var query = context.CreateQuery<T>(entitySet); foreach (var e in eagerProperties) { query = query.Expand(e); } return query; } public void SaveChanges() { this.context.SaveChanges(SaveChangesOptions.Batch); } } For instance, you can use the following code to retrieve customers with First name equal to “John”, and all their orders in a single call. repository.Retrieve<Customer>(    c => c.FirstName == “John”, //Where    c => c.Orders.SubExpand(o => o.Items)); In case, you want to have some pre-defined queries that you are going to use across several places, you can put them in an specific class. public static class CustomerQueries {   public static Expression<Func<Customer, bool>> LastNameEqualsTo(string lastName)   {     return c => c.LastName == lastName;   } } And then, use it with the repository. repository.Retrieve<Customer>(    CustomerQueries.LastNameEqualsTo("foo"),    c => c.Orders.SubExpand(o => o.Items));

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