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  • Review: Data Modeling 101

    I just recently read “Data Modeling 101”by Scott W. Ambler where he gave an overview of fundamental data modeling skills. I think this article was excellent for anyone who was just starting to learn or refresh their skills in regards to the modeling of data.  Scott defines data modeling as the act of exploring data oriented structures.  He goes on to explain about how data models are actually used by defining three different types of models. Types of Data Models Conceptual Data Model  Logical Data Model (LDMs) Physical Data Model(PDMs) He further expands on modeling by exploring common data modeling notations because there are no industry standards for the practice of data modeling. Scott then defines how to actually model data by expanding on entities, attributes, identities, and relationships which are the basic building blocks of data models. In addition he discusses the value of normalization for redundancy and demoralization for performance. Finally, he discuss ways in which Developers and DBAs can become better data modelers through the use of practice, and seeking guidance from more experienced data modelers.

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  • HTML5 data-* (custom data attribute)

    - by Renso
    Goal: Store custom data with the data attribute on any DOM element and retrieve it. Previously under HTML4 we used to use classes to store custom data, something to the affect of <input class="account void limit-5000 over-4999" /> and then have to parse the data out of the class In a book published by Peter-Paul Koch in 2007, ppk on JavaScript, he explains why and how to use custom attributes to make data more accessible to JavaScript, using name-value pairs. Accessing a custom attribute account-limit=5000 is much easier and more intuitive than trying to parse it out of a class, Plus, what if the class name for example "color-5" has a representative class definition in a CSS stylesheet that hides it away or worse some JavaScript plugin that automatically adds 5000 to it, or something crazy like that, just because it is a valid class name. As you can see there are quite a few reasons why using classes is a bad design and why it was important to define custom data attributes in HTML5. Syntax: You define the data attribute by simply prefixing any data item you want to store with any HTML element with "data-". For example to store our customers account data with a hidden input element: <input type="hidden" data-account="void" data-limit=5000 data-over=4999  /> How to access the data: account  -     element.dataset.account limit    -     element.dataset.limit You can also access it by using the more traditional get/setAttribute method or if using jQuery $('#element').attr('data-account','void') Browser support: All except for IE. There is an IE hack around this at http://gist.github.com/362081. Special Note: Be AWARE, do not use upper-case when defining your data elements as it is all converted to lower-case when reading it, so: data-myAccount="A1234" will not be found when you read it with: element.dataset.myAccount Use only lowercase when reading so this will work: element.dataset.myaccount

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  • Master Data Management – A Foundation for Big Data Analysis

    - by Manouj Tahiliani
    While Master Data Management has crossed the proverbial chasm and is on its way to becoming mainstream, businesses are being hammered by a new megatrend called Big Data. Big Data is characterized by massive volumes, its high frequency, the variety of less structured data sources such as email, sensors, smart meters, social networks, and Weblogs, and the need to analyze vast amounts of data to determine value to improve upon management decisions. Businesses that have embraced MDM to get a single, enriched and unified view of Master data by resolving semantic discrepancies and augmenting the explicit master data information from within the enterprise with implicit data from outside the enterprise like social profiles will have a leg up in embracing Big Data solutions. This is especially true for large and medium-sized businesses in industries like Retail, Communications, Financial Services, etc that would find it very challenging to get comprehensive analytical coverage and derive long-term success without resolving the limitations of the heterogeneous topology that leads to disparate, fragmented and incomplete master data. For analytical success from Big Data or in other words ROI from Big Data Investments, businesses need to acquire, organize and analyze the deluge of data to make better decisions. There will need to be a coexistence of structured and unstructured data and to maintain a tight link between the two to extract maximum insights. MDM is the catalyst that helps maintain that tight linkage by providing an understanding about the identity, characteristics of Persons, Companies, Products, Suppliers, etc. associated with the Big Data and thereby help accelerate ROI. In my next post I will discuss about patterns for co-existing Big Data Solutions and MDM. Feel free to provide comments and thoughts on above as well as Integration or Architectural patterns.

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  • How to Assure an Effective Data Model

    As a general rule in my opinion the effectiveness of a data model can be directly related to the accuracy and complexity of a project’s requirements. For example there is no need to work on very detailed data models when the details surrounding a specific data model have not been defined or even clarified. Developing data models when the clarity of project requirements is limited tends to introduce designed issues because the proper details to create an effective data model are not even known. One way to avoid this issue is to create data models that correspond to the complexity of the existing project requirements so that when requirements are updated then new data models can be created based any new discoveries regarding requirements on a fine grain level.  This allows for data models to be composed of general entities to be created initially when a project’s requirements are very vague and then the entities are refined as new and more substantial requirements are defined or redefined. This promotes communication amongst all stakeholders within a project as they go through the process of defining and finalizing project requirements.In addition, here are some general tips that can be applied to projects in regards to data modeling.Initially model all data generally and slowly reactor the data model as new requirements and business constraints are applied to a project.Ensure that data modelers have the proper tools and training they need to design a data model accurately.Create a common location for all project documents so that everyone will be able to review a project’s data models along with any other project documentation.All data models should follow a clear naming schema that tells readers the intended purpose for the data and how it is going to be applied within a project.

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  • Creating a shim Stream

    - by spender
    A decompression API that I am using has the following API: Decode(Stream inStream,Stream outStream) I'd like to create a wrapper around this API, such that I can create my own Stream class which offers up the decoded data. Stream decodedStream=new BlaDecodeStream(inStream); So that I can than use this stream as a parameter to the XmlReader constructor in the same way one might use the System.IO.Compression.GZipStream. As far as I can tell, the only other option is set outStream stream to a MemoryStream or to a FileStream and go in two hops. The files I am dealing with are enormous, so neither of these options are particularly attractive. Before I go reinventing the wheel, is there any prior art that I might be able to draw from, or something in the BCL I might have missed? The CircularStream implementation here would go some of the way to helping, but I'm really looking for something similar that would block (as opposed to over/underrun) when the Stream's internal buffer is 'empty' when reading from it and block when the internal buffer is full when writing to it. In this way it could serve as parameter outStream and simultaneously (i.e. from another thread) could be read from by the XmlReader.

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  • x-dom-event-stream in Opera 10 Only Working on First Event

    - by Brad
    I have a python script (in the CherryPy framework) that sends Event: and data: text as this Opera blog post describes to a client browser. The javascript that recieves the x-dom-event-stream content is almost identical to what they show in the blog post. However, the browser displays only the first event sent. Anyone know what I'm missing? I tried a few older versions of Opera and found that it works in Opera 9.52 but not in any newer versions. What did they change? Here is the python code: class dumpData(object): def index(self): cherrypy.response.headers['Content-Type'] = "application/x-dom-event-stream" def yieldData(): i = 0 while 1: yield "Event: count\n" yield "data: " yield i yield "\n\n" i = i + 1 time.sleep(3); return yieldData() index._cp_config = {'response.stream': True} index.exposed = True And here is the javascript/html. Making a request to /data/ runs the python function above. <head> <script> onload = function() { document.getElementById("count").addEventListener("cout", cout, false); } function count(e) { document.getElementById("stream").firstChild.nodeValue = e.data; } </script> <event-source id="count" src="/data/"> </head> <body> <div id="stream"></div> </body> Opening the direct /data/ url in Firefox saves the stream to a file. So I know the output is in the correct format and that the stream works at all.

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  • Why Stream/lazy val implementation using is faster than ListBuffer one

    - by anrizal
    I coded the following implementation of lazy sieve algorithms using Stream and lazy val below : def primes(): Stream[Int] = { lazy val ps = 2 #:: sieve(3) def sieve(p: Int): Stream[Int] = { p #:: sieve( Stream.from(p + 2, 2). find(i=> ps.takeWhile(j => j * j <= i). forall(i % _ > 0)).get) } ps } and the following implementation using (mutable) ListBuffer: import scala.collection.mutable.ListBuffer def primes(): Stream[Int] = { def sieve(p: Int, ps: ListBuffer[Int]): Stream[Int] = { p #:: { val nextprime = Stream.from(p + 2, 2). find(i=> ps.takeWhile(j => j * j <= i). forall(i % _ > 0)).get sieve(nextprime, ps += nextprime) } } sieve(3, ListBuffer(3))} When I did primes().takeWhile(_ < 1000000).size , the first implementation is 3 times faster than the second one. What's the explanation for this ? I edited the second version: it should have been sieve(3, ListBuffer(3)) instead of sieve(3, ListBuffer()) .

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  • Can't play stream from TorrentFlux stream

    - by thegreyspot
    Hi I am trying to stream a video from my TorrentFlux-b4rt server. I tried multiple media players, none work. Only VLC was able to produce an error message: input can't be opened: VLC is unable to open the MRL 'mms://...:8080/'. Check the log for details. I have tried multiple computers on different networks and all have the same issue. I am using Windows 7 to play the videos, and the server is Torrentflux-b4rt 1.0-beta2 with ubuntu 9.10. Thank you.

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  • SQL SERVER – Why Do We Need Data Quality Services – Importance and Significance of Data Quality Services (DQS)

    - by pinaldave
    Databases are awesome.  I’m sure my readers know my opinion about this – I have made SQL Server my life’s work after all!  I love technology and all things computer-related.  Of course, even with my love for technology, I have to admit that it has its limits.  For example, it takes a human brain to notice that data has been input incorrectly.  Computer “brains” might be faster than humans, but human brains are still better at pattern recognition.  For example, a human brain will notice that “300” is a ridiculous age for a human to be, but to a computer it is just a number.  A human will also notice similarities between “P. Dave” and “Pinal Dave,” but this would stump most computers. In a database, these sorts of anomalies are incredibly important.  Databases are often used by multiple people who rely on this data to be true and accurate, so data quality is key.  That is why the improved SQL Server features Master Data Management talks about Data Quality Services.  This service has the ability to recognize and flag anomalies like out of range numbers and similarities between data.  This allows a human brain with its pattern recognition abilities to double-check and ensure that P. Dave is the same as Pinal Dave. A nice feature of Data Quality Services is that once you set the rules for the program to follow, it will not only keep your data organized in the future, but go to the past and “fix up” any data that has already been entered.  It also allows you do combine data from multiple places and it will apply these rules across the board, so that you don’t have any weird issues that crop up when trying to fit a round peg into a square hole. There are two parts of Data Quality Services that help you accomplish all these neat things.  The first part is DQL Server, which you can think of as the hardware component of the system.  It is installed on the side of (it needs to install separately after SQL Server is installed) SQL Server and runs quietly in the background, performing all its cleanup services. DQS Client is the user interface that you can interact with to set the rules and check over your data.  There are three main aspects of Client: knowledge base management, data quality projects and administration.  Knowledge base management is the part of the system that allows you to set the rules, or program the “knowledge base,” so that your database is clean and consistent. Data Quality projects are what run in the background and clean up the data that is already present.  The administration allows you to check out what DQS Client is doing, change rules, and generally oversee the entire process.  The whole process is user-friendly and a pleasure to use.  I highly recommend implementing Data Quality Services in your database. Here are few of my blog posts which are related to Data Quality Services and I encourage you to try this out. SQL SERVER – Installing Data Quality Services (DQS) on SQL Server 2012 SQL SERVER – Step by Step Guide to Beginning Data Quality Services in SQL Server 2012 – Introduction to DQS SQL SERVER – DQS Error – Cannot connect to server – A .NET Framework error occurred during execution of user-defined routine or aggregate “SetDataQualitySessions” – SetDataQualitySessionPhaseTwo SQL SERVER – Configuring Interactive Cleansing Suggestion Min Score for Suggestions in Data Quality Services (DQS) – Sensitivity of Suggestion SQL SERVER – Unable to DELETE Project in Data Quality Projects (DQS) 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: Data Quality Services, DQS

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  • Welcome Oracle Data Integration 12c: Simplified, Future-Ready Solutions with Extreme Performance

    - by Irem Radzik
    Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 The big day for the Oracle Data Integration team has finally arrived! It is my honor to introduce you to Oracle Data Integration 12c. Today we announced the general availability of 12c release for Oracle’s key data integration products: Oracle Data Integrator 12c and Oracle GoldenGate 12c. The new release delivers extreme performance, increase IT productivity, and simplify deployment, while helping IT organizations to keep pace with new data-oriented technology trends including cloud computing, big data analytics, real-time business intelligence. With the 12c release Oracle becomes the new leader in the data integration and replication technologies as no other vendor offers such a complete set of data integration capabilities for pervasive, continuous access to trusted data across Oracle platforms as well as third-party systems and applications. Oracle Data Integration 12c release addresses data-driven organizations’ critical and evolving data integration requirements under 3 key themes: Future-Ready Solutions Extreme Performance Fast Time-to-Value       There are many new features that support these key differentiators for Oracle Data Integrator 12c and for Oracle GoldenGate 12c. In this first 12c blog post, I will highlight only a few:·Future-Ready Solutions to Support Current and Emerging Initiatives: Oracle Data Integration offer robust and reliable solutions for key technology trends including cloud computing, big data analytics, real-time business intelligence and continuous data availability. Via the tight integration with Oracle’s database, middleware, and application offerings Oracle Data Integration will continue to support the new features and capabilities right away as these products evolve and provide advance features. E    Extreme Performance: Both GoldenGate and Data Integrator are known for their high performance. The new release widens the gap even further against competition. Oracle GoldenGate 12c’s Integrated Delivery feature enables higher throughput via a special application programming interface into Oracle Database. As mentioned in the press release, customers already report up to 5X higher performance compared to earlier versions of GoldenGate. Oracle Data Integrator 12c introduces parallelism that significantly increases its performance as well. Fast Time-to-Value via Higher IT Productivity and Simplified Solutions:  Oracle Data Integrator 12c’s new flow-based declarative UI brings superior developer productivity, ease of use, and ultimately fast time to market for end users.  It also gives the ability to seamlessly reuse mapping logic speeds development.Oracle GoldenGate 12c ‘s Integrated Delivery feature automatically optimally tunes the process, saving time while improving performance. This is just a quick glimpse into Oracle Data Integrator 12c and Oracle GoldenGate 12c. On November 12th we will reveal much more about the new release in our video webcast "Introducing 12c for Oracle Data Integration". Our customer and partner speakers, including SolarWorld, BT, Rittman Mead will join us in launching the new release. Please join us at this free event to learn more from our executives about the 12c release, hear our customers’ perspectives on the new features, and ask your questions to our experts in the live Q&A. Also, please continue to follow our blogs, tweets, and Facebook updates as we unveil more about the new features of the latest release. /* 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-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

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  • Implementing a robust async stream reader for a console

    - by Jon
    I recently provided an answer to this question: C# - Realtime console output redirection. As often happens, explaining stuff (here "stuff" was how I tackled a similar problem) leads you to greater understanding and/or, as is the case here, "oops" moments. I realized that my solution, as implemented, has a bug. The bug has little practical importance, but it has an extremely large importance to me as a developer: I can't rest easy knowing that my code has the potential to blow up. Squashing the bug is the purpose of this question. I apologize for the long intro, so let's get dirty. I wanted to build a class that allows me to receive input from a Stream in an event-based manner. The stream, in my scenario, is guaranteed to be a FileStream and there is also an associated StreamReader already present to leverage. The public interface of the class is this: public class MyStreamManager { public event EventHandler<ConsoleOutputReadEventArgs> StandardOutputRead; public void StartSendingEvents(); public void StopSendingEvents(); } Obviously this specific scenario has to do with a console's standard output. StartSendingEvents and StopSendingEvents do what they advertise; for the purposes of this discussion, we can assume that events are always being sent without loss of generality. The class uses these two fields internally: protected readonly StringBuilder inputAccumulator = new StringBuilder(); protected readonly byte[] buffer = new byte[256]; The functionality of the class is implemented in the methods below. To get the ball rolling: public void StartSendingEvents(); { this.stopAutomation = false; this.BeginReadAsync(); } To read data out of the Stream without blocking, and also without requiring a carriage return char, BeginRead is called: protected void BeginReadAsync() { if (!this.stopAutomation) { this.StandardOutput.BaseStream.BeginRead( this.buffer, 0, this.buffer.Length, this.ReadHappened, null); } } The challenging part: BeginRead requires using a buffer. This means that when reading from the stream, it is possible that the bytes available to read ("incoming chunk") are larger than the buffer. Since we are only handing off data from the stream to a consumer, and that consumer may well have inside knowledge about the size and/or format of these chunks, I want to call event subscribers exactly once for each chunk. Otherwise the abstraction breaks down and the subscribers have to buffer the incoming data and reconstruct the chunks themselves using said knowledge. This is much less convenient to the calling code, and detracts from the usefulness of my class. Edit: There are comments below correctly stating that since the data is coming from a stream, there is absolutely nothing that the receiver can infer about the structure of the data unless it is fully prepared to parse it. What I am trying to do here is leverage the "flush the output" "structure" that the owner of the console imparts while writing on it. I am prepared to assume (better: allow my caller to have the option to assume) that the OS will pass me the data written between two flushes of the stream in exactly one piece. To this end, if the buffer is full after EndRead, we don't send its contents to subscribers immediately but instead append them to a StringBuilder. The contents of the StringBuilder are only sent back whenever there is no more to read from the stream (thus preserving the chunks). private void ReadHappened(IAsyncResult asyncResult) { var bytesRead = this.StandardOutput.BaseStream.EndRead(asyncResult); if (bytesRead == 0) { this.OnAutomationStopped(); return; } var input = this.StandardOutput.CurrentEncoding.GetString( this.buffer, 0, bytesRead); this.inputAccumulator.Append(input); if (bytesRead < this.buffer.Length) { this.OnInputRead(); // only send back if we 're sure we got it all } this.BeginReadAsync(); // continue "looping" with BeginRead } After any read which is not enough to fill the buffer, all accumulated data is sent to the subscribers: private void OnInputRead() { var handler = this.StandardOutputRead; if (handler == null) { return; } handler(this, new ConsoleOutputReadEventArgs(this.inputAccumulator.ToString())); this.inputAccumulator.Clear(); } (I know that as long as there are no subscribers the data gets accumulated forever. This is a deliberate decision). The good This scheme works almost perfectly: Async functionality without spawning any threads Very convenient to the calling code (just subscribe to an event) Maintains the "chunkiness" of the data; this allows the calling code to use inside knowledge of the data without doing any extra work Is almost agnostic to the buffer size (it will work correctly with any size buffer irrespective of the data being read) The bad That last almost is a very big one. Consider what happens when there is an incoming chunk with length exactly equal to the size of the buffer. The chunk will be read and buffered, but the event will not be triggered. This will be followed up by a BeginRead that expects to find more data belonging to the current chunk in order to send it back all in one piece, but... there will be no more data in the stream. In fact, as long as data is put into the stream in chunks with length exactly equal to the buffer size, the data will be buffered and the event will never be triggered. This scenario may be highly unlikely to occur in practice, especially since we can pick any number for the buffer size, but the problem is there. Solution? Unfortunately, after checking the available methods on FileStream and StreamReader, I can't find anything which lets me peek into the stream while also allowing async methods to be used on it. One "solution" would be to have a thread wait on a ManualResetEvent after the "buffer filled" condition is detected. If the event is not signaled (by the async callback) in a small amount of time, then more data from the stream will not be forthcoming and the data accumulated so far should be sent to subscribers. However, this introduces the need for another thread, requires thread synchronization, and is plain inelegant. Specifying a timeout for BeginRead would also suffice (call back into my code every now and then so I can check if there's data to be sent back; most of the time there will not be anything to do, so I expect the performance hit to be negligible). But it looks like timeouts are not supported in FileStream. Since I imagine that async calls with timeouts are an option in bare Win32, another approach might be to PInvoke the hell out of the problem. But this is also undesirable as it will introduce complexity and simply be a pain to code. Is there an elegant way to get around the problem? Thanks for being patient enough to read all of this.

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  • Android ListView with alternate color and on focus color

    - by Yogesh
    I need to set alternate color in list view rows but when i do that it removes/ disables the on focus default yellow background I tried with backgroundColor rowView.setBackgroundColor(SOME COLOR); also with backgrounddrwable. rowView.setBackgroundColor(R.drawable.view_odd_row_bg); <!-- Even though these two point to the same resource, have two states so the drawable will invalidate itself when coming out of pressed state. --> <item android:state_focused="true" android:state_enabled="false" android:state_pressed="true" android:drawable="@color/highlight" /> <item android:state_focused="true" android:state_enabled="false" android:drawable="@color/highlight" /> <item android:state_focused="true" android:state_pressed="true" android:drawable="@color/highlight" /> <item android:state_focused="false" android:state_pressed="true" android:drawable="@color/highlight" /> <item android:state_focused="true" android:drawable="@color/highlight" /> but it wont work. is there any way we can set background color and on focus color simultaneously which will work.

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  • Big Data – Final Wrap and What Next – Day 21 of 21

    - by Pinal Dave
    In yesterday’s blog post we explored various resources related to learning Big Data and in this blog post we will wrap up this 21 day series on Big Data. I have been exploring various terms and technology related to Big Data this entire month. It was indeed fun to write about Big Data in 21 days but the subject of Big Data is much bigger and larger than someone can cover it in 21 days. My first goal was to write about the basics and I think we have got that one covered pretty well. During this 21 days I have received many questions and answers related to Big Data. I have covered a few of the questions in this series and a few more I will be covering in the next coming months. Now after understanding Big Data basics. I am personally going to do a list of the things next. I thought I will share the same with you as this will give you a good idea how to continue the journey of the Big Data. Build a schedule to read various Apache documentations Watch all Pluralsight Courses Explore HortonWorks Sandbox Start building presentation about Big Data – this is a great way to learn something new Present in User Groups Meetings on Big Data Topics Write more blog posts about Big Data I am going to continue learning about Big Data – I want you to continue learning Big Data. Please leave a comment how you are going to continue learning about Big Data. I will publish all the informative comments on this blog with due credit. I want to end this series with the infographic by UMUC. 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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  • Partner Webcast - Focus on Oracle Data Profiling and Data Quality 11g

    - by lukasz.romaszewski(at)oracle.com
    Normal 0 false false false EN-US X-NONE X-NONE /* 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:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-ansi-language:RO;} Partner Webcast Focus on Oracle Data Profiling and Data Quality 11g February 24th, 12am  CET   Oracle offers an integrated suite Data Quality software architected to discover and correct today's data quality problems and establish a platform prepared for tomorrow's yet unknown data challenges. Oracle Data Profiling provides data investigation, discovery, and profiling in support of quality, migration, integration, stewardship, and governance initiatives. It includes a broad range of features that expand upon basic profiling, including automated monitoring, business-rule validation, and trend analysis. Oracle Data Quality for Data Integrator provides cleansing, standardization, matching, address validation, location enrichment, and linking functions for global customer data and operational business data. It ensures that data adheres to established standards that are adaptable to fit each organization's specific needs.  Both single - and double - byte data are processed in local languages to provide a unique and centralized view of customers, products and services.   During this in-person briefing, Data Integration Solution Specialists will be providing a technical overview and a walkthrough.   Agenda ·         Oracle Data Integration Strategy overview ·         A focus on Oracle Data Profiling and Oracle Data Quality for Data Integrator: o   Oracle Data Profiling o   Oracle Data Quality for Data Integrator o   Live demoo   Q&A Delivery Format  This FREE online LIVE eSeminar will be delivered over the Web and Conference Call. Registrations   received less than 24hours  prior to start time may not receive confirmation to attend. To register , click here. For any questions please contact [email protected]. 

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  • Using an alternate JSON Serializer in ASP.NET Web API

    - by Rick Strahl
    The new ASP.NET Web API that Microsoft released alongside MVC 4.0 Beta last week is a great framework for building REST and AJAX APIs. I've been working with it for quite a while now and I really like the way it works and the complete set of features it provides 'in the box'. It's about time that Microsoft gets a decent API for building generic HTTP endpoints into the framework. DataContractJsonSerializer sucks As nice as Web API's overall design is one thing still sucks: The built-in JSON Serialization uses the DataContractJsonSerializer which is just too limiting for many scenarios. The biggest issues I have with it are: No support for untyped values (object, dynamic, Anonymous Types) MS AJAX style Date Formatting Ugly serialization formats for types like Dictionaries To me the most serious issue is dealing with serialization of untyped objects. I have number of applications with AJAX front ends that dynamically reformat data from business objects to fit a specific message format that certain UI components require. The most common scenario I have there are IEnumerable query results from a database with fields from the result set rearranged to fit the sometimes unconventional formats required for the UI components (like jqGrid for example). Creating custom types to fit these messages seems like overkill and projections using Linq makes this much easier to code up. Alas DataContractJsonSerializer doesn't support it. Neither does DataContractSerializer for XML output for that matter. What this means is that you can't do stuff like this in Web API out of the box:public object GetAnonymousType() { return new { name = "Rick", company = "West Wind", entered= DateTime.Now }; } Basically anything that doesn't have an explicit type DataContractJsonSerializer will not let you return. FWIW, the same is true for XmlSerializer which also doesn't work with non-typed values for serialization. The example above is obviously contrived with a hardcoded object graph, but it's not uncommon to get dynamic values returned from queries that have anonymous types for their result projections. Apparently there's a good possibility that Microsoft will ship Json.NET as part of Web API RTM release.  Scott Hanselman confirmed this as a footnote in his JSON Dates post a few days ago. I've heard several other people from Microsoft confirm that Json.NET will be included and be the default JSON serializer, but no details yet in what capacity it will show up. Let's hope it ends up as the default in the box. Meanwhile this post will show you how you can use it today with the beta and get JSON that matches what you should see in the RTM version. What about JsonValue? To be fair Web API DOES include a new JsonValue/JsonObject/JsonArray type that allow you to address some of these scenarios. JsonValue is a new type in the System.Json assembly that can be used to build up an object graph based on a dictionary. It's actually a really cool implementation of a dynamic type that allows you to create an object graph and spit it out to JSON without having to create .NET type first. JsonValue can also receive a JSON string and parse it without having to actually load it into a .NET type (which is something that's been missing in the core framework). This is really useful if you get a JSON result from an arbitrary service and you don't want to explicitly create a mapping type for the data returned. For serialization you can create an object structure on the fly and pass it back as part of an Web API action method like this:public JsonValue GetJsonValue() { dynamic json = new JsonObject(); json.name = "Rick"; json.company = "West Wind"; json.entered = DateTime.Now; dynamic address = new JsonObject(); address.street = "32 Kaiea"; address.zip = "96779"; json.address = address; dynamic phones = new JsonArray(); json.phoneNumbers = phones; dynamic phone = new JsonObject(); phone.type = "Home"; phone.number = "808 123-1233"; phones.Add(phone); phone = new JsonObject(); phone.type = "Home"; phone.number = "808 123-1233"; phones.Add(phone); //var jsonString = json.ToString(); return json; } which produces the following output (formatted here for easier reading):{ name: "rick", company: "West Wind", entered: "2012-03-08T15:33:19.673-10:00", address: { street: "32 Kaiea", zip: "96779" }, phoneNumbers: [ { type: "Home", number: "808 123-1233" }, { type: "Mobile", number: "808 123-1234" }] } If you need to build a simple JSON type on the fly these types work great. But if you have an existing type - or worse a query result/list that's already formatted JsonValue et al. become a pain to work with. As far as I can see there's no way to just throw an object instance at JsonValue and have it convert into JsonValue dictionary. It's a manual process. Using alternate Serializers in Web API So, currently the default serializer in WebAPI is DataContractJsonSeriaizer and I don't like it. You may not either, but luckily you can swap the serializer fairly easily. If you'd rather use the JavaScriptSerializer built into System.Web.Extensions or Json.NET today, it's not too difficult to create a custom MediaTypeFormatter that uses these serializers and can replace or partially replace the native serializer. Here's a MediaTypeFormatter implementation using the ASP.NET JavaScriptSerializer:using System; using System.Net.Http.Formatting; using System.Threading.Tasks; using System.Web.Script.Serialization; using System.Json; using System.IO; namespace Westwind.Web.WebApi { public class JavaScriptSerializerFormatter : MediaTypeFormatter { public JavaScriptSerializerFormatter() { SupportedMediaTypes.Add(new System.Net.Http.Headers.MediaTypeHeaderValue("application/json")); } protected override bool CanWriteType(Type type) { // don't serialize JsonValue structure use default for that if (type == typeof(JsonValue) || type == typeof(JsonObject) || type== typeof(JsonArray) ) return false; return true; } protected override bool CanReadType(Type type) { if (type == typeof(IKeyValueModel)) return false; return true; } protected override System.Threading.Tasks.Taskobject OnReadFromStreamAsync(Type type, System.IO.Stream stream, System.Net.Http.Headers.HttpContentHeaders contentHeaders, FormatterContext formatterContext) { var task = Taskobject.Factory.StartNew(() = { var ser = new JavaScriptSerializer(); string json; using (var sr = new StreamReader(stream)) { json = sr.ReadToEnd(); sr.Close(); } object val = ser.Deserialize(json,type); return val; }); return task; } protected override System.Threading.Tasks.Task OnWriteToStreamAsync(Type type, object value, System.IO.Stream stream, System.Net.Http.Headers.HttpContentHeaders contentHeaders, FormatterContext formatterContext, System.Net.TransportContext transportContext) { var task = Task.Factory.StartNew( () = { var ser = new JavaScriptSerializer(); var json = ser.Serialize(value); byte[] buf = System.Text.Encoding.Default.GetBytes(json); stream.Write(buf,0,buf.Length); stream.Flush(); }); return task; } } } Formatter implementation is pretty simple: You override 4 methods to tell which types you can handle and then handle the input or output streams to create/parse the JSON data. Note that when creating output you want to take care to still allow JsonValue/JsonObject/JsonArray types to be handled by the default serializer so those objects serialize properly - if you let either JavaScriptSerializer or JSON.NET handle them they'd try to render the dictionaries which is very undesirable. If you'd rather use Json.NET here's the JSON.NET version of the formatter:// this code requires a reference to JSON.NET in your project #if true using System; using System.Net.Http.Formatting; using System.Threading.Tasks; using System.Web.Script.Serialization; using System.Json; using Newtonsoft.Json; using System.IO; using Newtonsoft.Json.Converters; namespace Westwind.Web.WebApi { public class JsonNetFormatter : MediaTypeFormatter { public JsonNetFormatter() { SupportedMediaTypes.Add(new System.Net.Http.Headers.MediaTypeHeaderValue("application/json")); } protected override bool CanWriteType(Type type) { // don't serialize JsonValue structure use default for that if (type == typeof(JsonValue) || type == typeof(JsonObject) || type == typeof(JsonArray)) return false; return true; } protected override bool CanReadType(Type type) { if (type == typeof(IKeyValueModel)) return false; return true; } protected override System.Threading.Tasks.Taskobject OnReadFromStreamAsync(Type type, System.IO.Stream stream, System.Net.Http.Headers.HttpContentHeaders contentHeaders, FormatterContext formatterContext) { var task = Taskobject.Factory.StartNew(() = { var settings = new JsonSerializerSettings() { NullValueHandling = NullValueHandling.Ignore, }; var sr = new StreamReader(stream); var jreader = new JsonTextReader(sr); var ser = new JsonSerializer(); ser.Converters.Add(new IsoDateTimeConverter()); object val = ser.Deserialize(jreader, type); return val; }); return task; } protected override System.Threading.Tasks.Task OnWriteToStreamAsync(Type type, object value, System.IO.Stream stream, System.Net.Http.Headers.HttpContentHeaders contentHeaders, FormatterContext formatterContext, System.Net.TransportContext transportContext) { var task = Task.Factory.StartNew( () = { var settings = new JsonSerializerSettings() { NullValueHandling = NullValueHandling.Ignore, }; string json = JsonConvert.SerializeObject(value, Formatting.Indented, new JsonConverter[1] { new IsoDateTimeConverter() } ); byte[] buf = System.Text.Encoding.Default.GetBytes(json); stream.Write(buf,0,buf.Length); stream.Flush(); }); return task; } } } #endif   One advantage of the Json.NET serializer is that you can specify a few options on how things are formatted and handled. You get null value handling and you can plug in the IsoDateTimeConverter which is nice to product proper ISO dates that I would expect any Json serializer to output these days. Hooking up the Formatters Once you've created the custom formatters you need to enable them for your Web API application. To do this use the GlobalConfiguration.Configuration object and add the formatter to the Formatters collection. Here's what this looks like hooked up from Application_Start in a Web project:protected void Application_Start(object sender, EventArgs e) { // Action based routing (used for RPC calls) RouteTable.Routes.MapHttpRoute( name: "StockApi", routeTemplate: "stocks/{action}/{symbol}", defaults: new { symbol = RouteParameter.Optional, controller = "StockApi" } ); // WebApi Configuration to hook up formatters and message handlers // optional RegisterApis(GlobalConfiguration.Configuration); } public static void RegisterApis(HttpConfiguration config) { // Add JavaScriptSerializer formatter instead - add at top to make default //config.Formatters.Insert(0, new JavaScriptSerializerFormatter()); // Add Json.net formatter - add at the top so it fires first! // This leaves the old one in place so JsonValue/JsonObject/JsonArray still are handled config.Formatters.Insert(0, new JsonNetFormatter()); } One thing to remember here is the GlobalConfiguration object which is Web API's static configuration instance. I think this thing is seriously misnamed given that GlobalConfiguration could stand for anything and so is hard to discover if you don't know what you're looking for. How about WebApiConfiguration or something more descriptive? Anyway, once you know what it is you can use the Formatters collection to insert your custom formatter. Note that I insert my formatter at the top of the list so it takes precedence over the default formatter. I also am not removing the old formatter because I still want JsonValue/JsonObject/JsonArray to be handled by the default serialization mechanism. Since they process in sequence and I exclude processing for these types JsonValue et al. still get properly serialized/deserialized. Summary Currently DataContractJsonSerializer in Web API is a pain, but at least we have the ability with relatively limited effort to replace the MediaTypeFormatter and plug in our own JSON serializer. This is useful for many scenarios - if you have existing client applications that used MVC JsonResult or ASP.NET AJAX results from ASMX AJAX services you can plug in the JavaScript serializer and get exactly the same serializer you used in the past so your results will be the same and don't potentially break clients. JSON serializers do vary a bit in how they serialize some of the more complex types (like Dictionaries and dates for example) and so if you're migrating it might be helpful to ensure your client code doesn't break when you switch to ASP.NET Web API. Going forward it looks like Microsoft is planning on plugging in Json.Net into Web API and make that the default. I think that's an awesome choice since Json.net has been around forever, is fast and easy to use and provides a ton of functionality as part of this great library. I just wish Microsoft would have figured this out sooner instead of now at the last minute integrating with it especially given that Json.Net has a similar set of lower level JSON objects JsonValue/JsonObject etc. which now will end up being duplicated by the native System.Json stuff. It's not like we don't already have enough confusion regarding which JSON serializer to use (JavaScriptSerializer, DataContractJsonSerializer, JsonValue/JsonObject/JsonArray and now Json.net). For years I've been using my own JSON serializer because the built in choices are both limited. However, with an official encorsement of Json.Net I'm happily moving on to use that in my applications. Let's see and hope Microsoft gets this right before ASP.NET Web API goes gold.© Rick Strahl, West Wind Technologies, 2005-2012Posted in Web Api  AJAX  ASP.NET   Tweet !function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0];if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src="//platform.twitter.com/widgets.js";fjs.parentNode.insertBefore(js,fjs);}}(document,"script","twitter-wjs"); (function() { var po = document.createElement('script'); po.type = 'text/javascript'; po.async = true; po.src = 'https://apis.google.com/js/plusone.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(po, s); })();

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  • Big Data – Buzz Words: What is HDFS – Day 8 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned what is MapReduce. In this article we will take a quick look at one of the four most important buzz words which goes around Big Data – HDFS. What is HDFS ? HDFS stands for Hadoop Distributed File System and it is a primary storage system used by Hadoop. It provides high performance access to data across Hadoop clusters. It is usually deployed on low-cost commodity hardware. In commodity hardware deployment server failures are very common. Due to the same reason HDFS is built to have high fault tolerance. The data transfer rate between compute nodes in HDFS is very high, which leads to reduced risk of failure. HDFS creates smaller pieces of the big data and distributes it on different nodes. It also copies each smaller piece to multiple times on different nodes. Hence when any node with the data crashes the system is automatically able to use the data from a different node and continue the process. This is the key feature of the HDFS system. Architecture of HDFS The architecture of the HDFS is master/slave architecture. An HDFS cluster always consists of single NameNode. This single NameNode is a master server and it manages the file system as well regulates access to various files. In additional to NameNode there are multiple DataNodes. There is always one DataNode for each data server. In HDFS a big file is split into one or more blocks and those blocks are stored in a set of DataNodes. The primary task of the NameNode is to open, close or rename files and directory and regulate access to the file system, whereas the primary task of the DataNode is read and write to the file systems. DataNode is also responsible for the creation, deletion or replication of the data based on the instruction from NameNode. In reality, NameNode and DataNode are software designed to run on commodity machine build in Java language. Visual Representation of HDFS Architecture Let us understand how HDFS works with the help of the diagram. Client APP or HDFS Client connects to NameSpace as well as DataNode. Client App access to the DataNode is regulated by NameSpace Node. NameSpace Node allows Client App to connect to the DataNode based by allowing the connection to the DataNode directly. A big data file is divided into multiple data blocks (let us assume that those data chunks are A,B,C and D. Client App will later on write data blocks directly to the DataNode. Client App does not have to directly write to all the node. It just has to write to any one of the node and NameNode will decide on which other DataNode it will have to replicate the data. In our example Client App directly writes to DataNode 1 and detained 3. However, data chunks are automatically replicated to other nodes. All the information like in which DataNode which data block is placed is written back to NameNode. High Availability During Disaster Now as multiple DataNode have same data blocks in the case of any DataNode which faces the disaster, the entire process will continue as other DataNode will assume the role to serve the specific data block which was on the failed node. This system provides very high tolerance to disaster and provides high availability. If you notice there is only single NameNode in our architecture. If that node fails our entire Hadoop Application will stop performing as it is a single node where we store all the metadata. As this node is very critical, it is usually replicated on another clustered as well as on another data rack. Though, that replicated node is not operational in architecture, it has all the necessary data to perform the task of the NameNode in the case of the NameNode fails. The entire Hadoop architecture is built to function smoothly even there are node failures or hardware malfunction. It is built on the simple concept that data is so big it is impossible to have come up with a single piece of the hardware which can manage it properly. We need lots of commodity (cheap) hardware to manage our big data and hardware failure is part of the commodity servers. To reduce the impact of hardware failure Hadoop architecture is built to overcome the limitation of the non-functioning hardware. Tomorrow In tomorrow’s blog post we will discuss the importance of the relational database in 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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  • how to watch??? England vs Belgium live stream Soccer friendly match ESPN

    - by Dada Fafa
    Do you want to watch:England vs Belgium live streaming online on pc?Searching for a good way to watch England vs Belgium live streaming online today? You've come to the right place!We'll show you how to watch England vs BelgiumNBA games live stream online in perfect high definition quality using any PC or Mac computer! It's possible! Now you can watch every minute of England vs Belgiumonline live,and in true HD quality no matter where you are! WANNA WATCH England vs Belgium LIVE STREAM MATCH

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  • Oracle Data Integration 12c: Perspectives of Industry Experts, Customers and Partners

    - by Irem Radzik
    Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 As you may have seen from our recent blog posts on Oracle Data Integrator 12c and Oracle GoldenGate 12c, we are very excited to share with you the great new features the 12c release brings to Oracle’s data integration solutions. And, fortunately we are not alone in this sentiment. Since the press announcement October 17th, which incorporates our customers' and experts' testimonials, we have seen positive comments in leading technology publications and social media as well. Here are some examples: In CIO and PCWorld you can find Joab Jackson’s article, Oracle Data Integrator 12c ready for real-time analysis, where wrote about the tight integration between Oracle Data Integrator and Oracle GoldenGate . He noted “Heeding the call from enterprise customers who clamor for more immediacy in their data-driven reports, Oracle has updated its data-integration software portfolio so that it can more rapidly deliver data to data warehouses and analysis applications.” Integration Developer News’ Vance McCarthy wrote the article Oracle Ships ‘Future Proofs’ Integration Tools for Traditional, Cloud, Big Data, Real-Time Projects and mentioned that “Oracle Data Integrator 12c and Oracle GoldenGate 12c sport a wide range of improvements to let devs more easily deliver data integration for cloud, analytics, big data and other new projects that leverage multiple datasets for business.“ InformationWeek’s Doug Henschen gave a great overview to several key features including the new flow-based UI in Oracle Data Integrator. Doug said “Oracle Data Integrator 12c introduces a complete makeover of the job-building experience, while real-time oriented GoldenGate 12c introduces performance gains “. In Database Trends and Applications’ article Oracle Strengthens Data Integration with Release of Oracle Data Integrator 12c and Oracle GoldenGate 12c highlighted the productivity aspect of the new solution with his remarks: “tight integration between Oracle Data Integrator 12c and Oracle GoldenGate 12c enables developers to leverage Oracle GoldenGate’s low overhead, real-time change data capture completely within the Oracle Data Integrator Studio without additional training”. We are also thrilled about what our customers and partners have to say about our products and the new release. And we are equally excited to share those perspectives with you in our upcoming launch video webcast on November 12th. SolarWorld Industries America’s Senior Database Manager, Russ Toyama will join our executives in our studio in Redwood Shores to discuss GoldenGate’s core benefits and the new release, while Surren Partharb, CTO of Strategic Technology Services for BT, and Mark Rittman, CTO of Rittman Mead, will provide their comments via the interviews conducted in the UK. This interactive panel discussion in the video webcast will unveil the new release with the expertise of our development executives and the great insight from our customers and partners. In addition, our product experts will be available online to answer chat questions. This is really a great opportunity to learn how Oracle's data integration offering has changed the integration and replication technology space with the new release, and established itself as the new leader. If you have not registered for this free event yet, you can do so via this link. We will run the live event at 8am PT/4pm GMT, followed by a replay of the event with live chat for Q&A  at 10am PT/6pm GMT. The replay will be available on-demand for those who register but cannot attend either session on November 12th. /* 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-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Times New Roman","serif"; mso-fareast-font-family:"Times New Roman";}

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  • Recording slow web stream

    - by Budric
    I'm trying to record an mpeg2 video stream from a website that doesn't have the greatest bandwidth. The video often buffers. I want to download the stream and watch it offline. The extract stream format received is: Stream #0.0[0x44]: Audio: mp2, 48000 Hz, stereo, s16, 192 kb/s Stream #0.1[0x45]: Video: mpeg2video (Main), yuv420p, 704x576 [PAR 16:11 DAR 16:9], 15000 kb/s, 27.19 fps, 25 tbr, 90k tbn, 50 tbc I use the following tool to transocde the stream: ffmpeg -i "http://url" -y -vcodec libx264 -b 3000k -acodec copy /tmp/stream.mp4 Unfortunately after a few seconds ffmpeg stops recording with an error [mpegts @ 0x1f0b9c0] PES packet size mismatch [mp2 @ 0x1f14640] incomplete frame Error while decoding stream #0.0 [mpeg2video @ 0x1f16860] ac-tex damaged at 0 26 [mpeg2video @ 0x1f16860] Warning MVs not available I've tried encoding with vlc as well with similar issues. Although vlc doesn't stop encoding, the output video has regions where it hangs. vlc -I dummy "http://url" --network-caching="1000" --sout="#transcode{vcodec=h264,vb=3000,acodec=mp3,ab=192}:std{access=file,mux=mp4,dst=/tmp/stream.mp4}" [mpeg2video @ 0x7f2d4c001e20] ac-tex damaged at 9 33 [mpeg2video @ 0x7f2d4c001e20] Warning MVs not available [mpeg2video @ 0x7f2d4c001e20] concealing 132 DC, 132 AC, 132 MV errors [mpeg2video @ 0x7f2d4c001e20] ac-tex damaged at 16 17 [mpeg2video @ 0x7f2d4c001e20] Warning MVs not available [mpeg2video @ 0x7f2d4c001e20] concealing 836 DC, 836 AC, 836 MV errors libdvbpsi error (PSI decoder): TS discontinuity (received 4, expected 3) for PID 0 I also tried flv transcoding and it shows up with its own set of issues, like output flv file hangs in certain parts. Anyone know what's wrong or how to fix this?

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  • Writing String to Stream and reading it back does not work

    - by Binary255
    I want to write a String to a Stream (a MemoryStream in this case) and read the bytes one by one. stringAsStream = new MemoryStream(); UnicodeEncoding uniEncoding = new UnicodeEncoding(); String message = "Message"; stringAsStream.Write(uniEncoding.GetBytes(message), 0, message.Length); Console.WriteLine("This:\t\t" + (char)uniEncoding.GetBytes(message)[0]); Console.WriteLine("Differs from:\t" + (char)stringAsStream.ReadByte()); The (undesired) result I get is: This: M Differs from: ? It looks like it's not being read correctly, as the first char of "Message" is 'M', which works when getting the bytes from the UnicodeEncoding instance but not when reading them back from the stream. What am I doing wrong? The bigger picture: I have an algorithm which will work on the bytes of a Stream, I'd like to be as general as possible and work with any Stream. I'd like to convert an ASCII-String into a MemoryStream, or maybe use another method to be able to work on the String as a Stream. The algorithm in question will work on the bytes of the Stream.

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  • New Big Data Appliance Security Features

    - by mgubar
    The Oracle Big Data Appliance (BDA) is an engineered system for big data processing.  It greatly simplifies the deployment of an optimized Hadoop Cluster – whether that cluster is used for batch or real-time processing.  The vast majority of BDA customers are integrating the appliance with their Oracle Databases and they have certain expectations – especially around security.  Oracle Database customers have benefited from a rich set of security features:  encryption, redaction, data masking, database firewall, label based access control – and much, much more.  They want similar capabilities with their Hadoop cluster.    Unfortunately, Hadoop wasn’t developed with security in mind.  By default, a Hadoop cluster is insecure – the antithesis of an Oracle Database.  Some critical security features have been implemented – but even those capabilities are arduous to setup and configure.  Oracle believes that a key element of an optimized appliance is that its data should be secure.  Therefore, by default the BDA delivers the “AAA of security”: authentication, authorization and auditing. Security Starts at Authentication A successful security strategy is predicated on strong authentication – for both users and software services.  Consider the default configuration for a newly installed Oracle Database; it’s been a long time since you had a legitimate chance at accessing the database using the credentials “system/manager” or “scott/tiger”.  The default Oracle Database policy is to lock accounts thereby restricting access; administrators must consciously grant access to users. Default Authentication in Hadoop By default, a Hadoop cluster fails the authentication test. For example, it is easy for a malicious user to masquerade as any other user on the system.  Consider the following scenario that illustrates how a user can access any data on a Hadoop cluster by masquerading as a more privileged user.  In our scenario, the Hadoop cluster contains sensitive salary information in the file /user/hrdata/salaries.txt.  When logged in as the hr user, you can see the following files.  Notice, we’re using the Hadoop command line utilities for accessing the data: $ hadoop fs -ls /user/hrdataFound 1 items-rw-r--r--   1 oracle supergroup         70 2013-10-31 10:38 /user/hrdata/salaries.txt$ hadoop fs -cat /user/hrdata/salaries.txtTom Brady,11000000Tom Hanks,5000000Bob Smith,250000Oprah,300000000 User DrEvil has access to the cluster – and can see that there is an interesting folder called “hrdata”.  $ hadoop fs -ls /user Found 1 items drwx------   - hr supergroup          0 2013-10-31 10:38 /user/hrdata However, DrEvil cannot view the contents of the folder due to lack of access privileges: $ hadoop fs -ls /user/hrdata ls: Permission denied: user=drevil, access=READ_EXECUTE, inode="/user/hrdata":oracle:supergroup:drwx------ Accessing this data will not be a problem for DrEvil. He knows that the hr user owns the data by looking at the folder’s ACLs. To overcome this challenge, he will simply masquerade as the hr user. On his local machine, he adds the hr user, assigns that user a password, and then accesses the data on the Hadoop cluster: $ sudo useradd hr $ sudo passwd $ su hr $ hadoop fs -cat /user/hrdata/salaries.txt Tom Brady,11000000 Tom Hanks,5000000 Bob Smith,250000 Oprah,300000000 Hadoop has not authenticated the user; it trusts that the identity that has been presented is indeed the hr user. Therefore, sensitive data has been easily compromised. Clearly, the default security policy is inappropriate and dangerous to many organizations storing critical data in HDFS. Big Data Appliance Provides Secure Authentication The BDA provides secure authentication to the Hadoop cluster by default – preventing the type of masquerading described above. It accomplishes this thru Kerberos integration. Figure 1: Kerberos Integration The Key Distribution Center (KDC) is a server that has two components: an authentication server and a ticket granting service. The authentication server validates the identity of the user and service. Once authenticated, a client must request a ticket from the ticket granting service – allowing it to access the BDA’s NameNode, JobTracker, etc. At installation, you simply point the BDA to an external KDC or automatically install a highly available KDC on the BDA itself. Kerberos will then provide strong authentication for not just the end user – but also for important Hadoop services running on the appliance. You can now guarantee that users are who they claim to be – and rogue services (like fake data nodes) are not added to the system. It is common for organizations to want to leverage existing LDAP servers for common user and group management. Kerberos integrates with LDAP servers – allowing the principals and encryption keys to be stored in the common repository. This simplifies the deployment and administration of the secure environment. Authorize Access to Sensitive Data Kerberos-based authentication ensures secure access to the system and the establishment of a trusted identity – a prerequisite for any authorization scheme. Once this identity is established, you need to authorize access to the data. HDFS will authorize access to files using ACLs with the authorization specification applied using classic Linux-style commands like chmod and chown (e.g. hadoop fs -chown oracle:oracle /user/hrdata changes the ownership of the /user/hrdata folder to oracle). Authorization is applied at the user or group level – utilizing group membership found in the Linux environment (i.e. /etc/group) or in the LDAP server. For SQL-based data stores – like Hive and Impala – finer grained access control is required. Access to databases, tables, columns, etc. must be controlled. And, you want to leverage roles to facilitate administration. Apache Sentry is a new project that delivers fine grained access control; both Cloudera and Oracle are the project’s founding members. Sentry satisfies the following three authorization requirements: Secure Authorization:  the ability to control access to data and/or privileges on data for authenticated users. Fine-Grained Authorization:  the ability to give users access to a subset of the data (e.g. column) in a database Role-Based Authorization:  the ability to create/apply template-based privileges based on functional roles. With Sentry, “all”, “select” or “insert” privileges are granted to an object. The descendants of that object automatically inherit that privilege. A collection of privileges across many objects may be aggregated into a role – and users/groups are then assigned that role. This leads to simplified administration of security across the system. Figure 2: Object Hierarchy – granting a privilege on the database object will be inherited by its tables and views. Sentry is currently used by both Hive and Impala – but it is a framework that other data sources can leverage when offering fine-grained authorization. For example, one can expect Sentry to deliver authorization capabilities to Cloudera Search in the near future. Audit Hadoop Cluster Activity Auditing is a critical component to a secure system and is oftentimes required for SOX, PCI and other regulations. The BDA integrates with Oracle Audit Vault and Database Firewall – tracking different types of activity taking place on the cluster: Figure 3: Monitored Hadoop services. At the lowest level, every operation that accesses data in HDFS is captured. The HDFS audit log identifies the user who accessed the file, the time that file was accessed, the type of access (read, write, delete, list, etc.) and whether or not that file access was successful. The other auditing features include: MapReduce:  correlate the MapReduce job that accessed the file Oozie:  describes who ran what as part of a workflow Hive:  captures changes were made to the Hive metadata The audit data is captured in the Audit Vault Server – which integrates audit activity from a variety of sources, adding databases (Oracle, DB2, SQL Server) and operating systems to activity from the BDA. Figure 4: Consolidated audit data across the enterprise.  Once the data is in the Audit Vault server, you can leverage a rich set of prebuilt and custom reports to monitor all the activity in the enterprise. In addition, alerts may be defined to trigger violations of audit policies. Conclusion Security cannot be considered an afterthought in big data deployments. Across most organizations, Hadoop is managing sensitive data that must be protected; it is not simply crunching publicly available information used for search applications. The BDA provides a strong security foundation – ensuring users are only allowed to view authorized data and that data access is audited in a consolidated framework.

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  • How to present a stable data model in a public API that allows internal data structures to be changed without breaking the public view of the data?

    - by Max Palmer
    I am in the process of developing an application that allows users to write C# scripts. These scripts allow users to call selected methods and to access and manipulate data in a document. This works well, however, in the development version, scripts access the document's (internal) data structures directly. This means that if we were to change the internal data model/structure, there is a good chance that someone's script will no longer compile. We obviously want to prevent this breaking change from happening, but still want to allow the user to write sensible C# code (whilst not restricting how we develop our internal data model as a result). We therefore need to decouple our scripting API and its data structures from our internal methods and data structures. We've a few ideas as to how we might allow the user to access a what is effectively a stable public version of the document's internal data*, but I wanted to throw the question out there to someone who might have some real experience of this problem. NB our internal document's data structure is quite complex and it could be quite difficult to wrap. We know we want to expose as little as possible in our public API, especially as once it's out there, it's out there for good. Can anyone help? How do scripting languages / APIs decouple their public API and data structures from their internal data structures? Is there no real alternative to having to write a complex interaction layer? If we need to do this, what's a good approach or pattern for wrapping complex data structures that include nested objects, including collections? I've looked at the API facade pattern, which looks like it's trying to address these kinds of issues, but are there alternatives? *One idea is to build a data facade that is kept stable across versions of our application. The facade exposes a set of facade data objects that are used in the script code. These maintain backwards compatibility and wrap access to our internal document's data model.

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  • Why Cornell University Chose Oracle Data Masking

    - by Troy Kitch
    One of the eight Ivy League schools, Cornell University found itself in the unfortunate position of having to inform over 45,000 University community members that their personal information had been breached when a laptop was stolen. To ensure this wouldn’t happen again, Cornell took steps to ensure that data used for non-production purposes is de-identified with Oracle Data Masking. A recent podcast highlights why organizations like Cornell are choosing Oracle Data Masking to irreversibly de-identify production data for use in non-production environments. Organizations often copy production data, that contains sensitive information, into non-production environments so they can test applications and systems using “real world” information. Data in non-production has increasingly become a target of cyber criminals and can be lost or stolen due to weak security controls and unmonitored access. Similar to production environments, data breaches in non-production environments can cost millions of dollars to remediate and cause irreparable harm to reputation and brand. Cornell’s applications and databases help carry out the administrative and academic mission of the university. They are running Oracle PeopleSoft Campus Solutions that include highly sensitive faculty, student, alumni, and prospective student data. This data is supported and accessed by a diverse set of developers and functional staff distributed across the university. Several years ago, Cornell experienced a data breach when an employee’s laptop was stolen.  Centrally stored backup information indicated there was sensitive data on the laptop. With no way of knowing what the criminal intended, the university had to spend significant resources reviewing data, setting up service centers to handle constituent concerns, and provide free credit checks and identity theft protection services—all of which cost money and took time away from other projects. To avoid this issue in the future Cornell came up with several options; one of which was to sanitize the testing and training environments. “The project management team was brought in and they developed a project plan and implementation schedule; part of which was to evaluate competing products in the market-space and figure out which one would work best for us.  In the end we chose Oracle’s solution based on its architecture and its functionality.” – Tony Damiani, Database Administration and Business Intelligence, Cornell University The key goals of the project were to mask the elements that were identifiable as sensitive in a consistent and efficient manner, but still support all the previous activities in the non-production environments. Tony concludes,  “What we saw was a very minimal impact on performance. The masking process added an additional three hours to our refresh window, but it was well worth that time to secure the environment and remove the sensitive data. I think some other key points you can keep in mind here is that there was zero impact on the production environment. Oracle Data Masking works in non-production environments only. Additionally, the risk of exposure has been significantly reduced and the impact to business was minimal.” With Oracle Data Masking organizations like Cornell can: Make application data securely available in non-production environments Prevent application developers and testers from seeing production data Use an extensible template library and policies for data masking automation Gain the benefits of referential integrity so that applications continue to work Listen to the podcast to hear the complete interview.  Learn more about Oracle Data Masking by registering to watch this SANS Institute Webcast and view this short demo.

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  • Removing Barriers to Create Effective Data Models

    After years of creating and maintaining data models, I have started to notice common barriers that decrease the accuracy and usefulness of models. In my opinion, the main causes of these barriers are the lack of knowledge and communication from within a company. The lack of knowledge in regards to data models or data modeling can take many forms. Company Culture Knowledge Whether documented or undocumented, existing business rules of a company can affect how data is modeled. For example, if a company only allows 1 assigned person per customer to be able to manipulate a customer’s record then then a data model that includes an associated table that joins customers and employee’s would be unneeded because that would allow for the possibility of multiple employees to handle a customer because of the potential for a many to many relationship between Customers and Employees. Technical Knowledge Depending on the data modeler’s proficiency in modeling data they can inadvertently cause issues and/or complications with a design without even noticing. It is important that companies share data modeling responsibilities so that the models are developed from multiple perspectives of a system, company and the original problem.  In addition, the tools that a company selects to create data models can also affect the accuracy of the model if designer are not familiar with the tools or the tools are too complex to use for the designer. Existing System Knowledge In order for a data modeler to model data for an existing system so that new changes can be applied to a system then they need to at least know the basic concepts of a system so that they can work within it. This will promote reusability of data and prevent the chance of duplicating data. Project Knowledge This should be pretty obvious, but it is very hard to create an accurate data model without knowing what data needs to be modeled. I have always found it strange that I have been asked to start modeling data prior to a client formalizing any requirements. Usually when this happens I have to make several iterations to a model, and the client still does not know exactly what they want.  In addition additional issues can arise when certain stakeholders of a project are not consulted prior to the design or after the project is over because it can cause miss understandings and confusion by the end user as well as possibly not solving the original problem for which a project is intended to solve. One common thread between each type of knowledge is that they can all be avoided through the use of good communication. For example, if a modeler is new to a company then they should ask older employees about any business specific rules that may be documented or undocumented that must be applied to projects in general. Furthermore, if a modeler is not really familiar with a specific data modeling software then they need to speak up and ask for help form other employees or their manager. This will not only help the modeler in the project, but also help them in future projects that they do for the company. Additionally, if a project is not clearly defined prior to a data modeler being assigned the modeling project then it is their responsibility to communicate with the other stakeholders to clarify any part of a project that is unclear so that the data model that is created is accurately aligned with a project.

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