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• Data Flow Diagram

  - by Nilesh

  Can anyone help me to draw a data flow diagram for a travel request form for a company in which an employee can request for travel and request approval by his/her by project manager and HR department. Regards Nils

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• Java Flow Control Problem

  - by Kyle_Solo

  I am programming a simple 2d game engine. I've decided how I'd like the engine to function: it will be composed of objects containing "events" that my main game loop will trigger when appropriate. A little more about the structure: Every GameObject has an updateEvent method. objectList is a list of all the objects that will receive update events. Only objects on this list have their updateEvent method called by the game loop. I’m trying to implement this method in the GameObject class (This specification is what I’d like the method to achieve): /** * This method removes a GameObject from objectList. The GameObject * should immediately stop executing code, that is, absolutely no more * code inside update events will be executed for the removed game object. * If necessary, control should transfer to the game loop. * @param go The GameObject to be removed */ public void remove(GameObject go) So if an object tries to remove itself inside of an update event, control should transfer back to the game engine: public void updateEvent() { //object's update event remove(this); System.out.println("Should never reach here!"); } Here’s what I have so far. It works, but the more I read about using exceptions for flow control the less I like it, so I want to see if there are alternatives. Remove Method public void remove(GameObject go) { //add to removedList //flag as removed //throw an exception if removing self from inside an updateEvent } Game Loop for(GameObject go : objectList) { try { if (!go.removed) { go.updateEvent(); } else { //object is scheduled to be removed, do nothing } } catch(ObjectRemovedException e) { //control has been transferred back to the game loop //no need to do anything here } } // now remove the objects that are in removedList from objectList 2 questions: Am I correct in assuming that the only way to implement the stop-right-away part of the remove method as described above is by throwing a custom exception and catching it in the game loop? (I know, using exceptions for flow control is like goto, which is bad. I just can’t think of another way to do what I want!) For the removal from the list itself, it is possible for one object to remove one that is farther down on the list. Currently I’m checking a removed flag before executing any code, and at the end of each pass removing the objects to avoid concurrent modification. Is there a better, preferably instant/non-polling way to do this?

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• Excel Help: Data Input Help

  - by B-Ballerl

  Everyday I download data from a site that will have rows each filled with individual data for clients. I'm able to input the data into excel as a whole but after that I'm having trouble figuring out how to put it into a chart. For example Web visits time. So say Client 1 stayed for 5 min increasing his total time on the site to 20 min and Client 2 stayed for 0 min keeping his time of 10 min and they were both registered on new years eve, and R1's last login was today and R2's was yesterday. (R for some reason repersents Client, no idea why...). Client 3 hasn't been on since he registered keeping his total at 4 min So my data would look something like this for Today (20110104) R1,20101231,20110104,20 R2,20101231,20110103,10 R3,20101231,20101231,4 And this for the day before (201101030), R1,20101231,20110102,15 R2,20101231,20110103,10 R3,20101231,20101231,4 I get about 200+ client rows each day where even the names of the Client list are changing. Is it possible to import the data each day and fill it in a excel sheet where the Client number is off on the left hand side in a table, and the amount of time (Whole Number ex. 4) each day it spends on the site extend to the right under it's specific date see Picture? I've manage to create a manual sheet but have been unsucessful at getting excel to do any of it for me. Here are two pictures:

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• Recover data from hard drive with partitions (but not most data) overwritten

  - by Macha

  I have a 500GB hard drive I've been keeping around to recover data from that I removed from a failing NAS drive that got sort of... erratic at the end. I finally got rid of the NAS when during a firmware update it removed the partition table. Fast forward to a week ago, when I was building a new PC, and a mixup resulted in me placing the hard drive in question in the new PC and installing Windows XP on the first 100GB. I'm presuming any data on that first 100GB is now gone, but for the rest of it, is there any way I can recover it at home, as professional data recovery is currently too expensive? I have a blank 1TB HDD if I can store any images of that hard drive on. The problem was definitely with the NAS and not the hard drive, as the hard drive had a successful install of Windows when mistakenly place in the new PC, and there were capacitors in the NAS's circuitry clearly broken. The data I want to recover (in order of priority) is: High: Some jpgs of family photos. Medium: Some RAW files. (There are also jpg versions of all of these) Low: Some mp3s, avis and ISOs, I can re-rip most of these if need be, but it'd be handy not to have to. (I don't need a backup lecture, and if you can hold it in from nagging Jeff Atwood for it, you can hold it in from nagging me for it) In short: The partition tables are gone and overwritten. The data is not overwritten, except for an amount equal to the size of a Windows XP SP3 installation.

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• Why Oracle Delivers More Value than IBM in Data Integration Solutions

  - by irem.radzik(at)oracle.com

  For data integration projects, IT organization look for a robust but an easy-to-use solution, which simplifies enterprise data architecture while providing exceptional value-- not one that adds complexity and costs. This is a major challenge today for customers who are using IBM InfoSphere products like DataStage or Change Data Capture. Whereas, Oracle consistently delivers higher level value with its data integration products such as Oracle Data Integrator, Oracle GoldenGate. There are many differentiators for Oracle's Data Integration offering in comparison to IBM. Here are the top five: Lower cost of ownership Higher performance in both real-time and bulk data movement Ease of use and flexibility Reliability Complete, Open, and Integrated Middleware Offering Architectural differences between products contribute a great deal to these differences. First of all, Oracle's ETL architecture does not require a middle-tier transformation server, something IBM does require. Not only it costs more to manage an additional transformation server including energy costs, but it adds a performance bottleneck as well. In addition, IBM's data integration products are complex and often require lengthy professional services engagements to integrate. This translates to higher costs and delayed time to market. Then there's the reliability factor. Our customers choose Oracle GoldenGate over IBM's InfoSphere Change Data Capture product because Oracle GoldenGate is designed for mission-critical systems that require guaranteed data delivery and automatic recovery in case of process interruptions. On Thursday we will discuss these key differentiators in detail and provide customer examples that chose Oracle over IBM in data integration projects. Join us on Thursday Feb 10th at 11am PT to learn how Oracle delivers more value than IBM in data integration solutions.

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• Sorting data in the SSIS Pipeline (Video)

  In this post I want to show a couple of ways to order the data that comes into the pipeline.  a number of people have asked me about this primarily because there are a number of ways to do it but also because some components in the pipeline take sorted inputs.  One of the methods I show is visually easy to understand and the other is less visual but potentially more performant.

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• Big GRC: Turning Data into Actionable GRC Intelligence

  - by Jenna Danko

  While it’s no longer headline news that Governments have carried out large scale data-mining programmes aimed at terrorism detection and identifying other patterns of interest across a wide range of digital data sources, the debate over the ethics and justification over this action, will clearly continue for some time to come. What is becoming clear is that these programmes are a framework for the collation and aggregation of massive amounts of unstructured data and from this, the creation of actionable intelligence from analyses that allowed the analysts to explore and extract a variety of patterns and then direct resources. This data included audio and video chats, phone calls, photographs, e-mails, documents, internet searches, social media posts and mobile phone logs and connections. Although Governance, Risk and Compliance (GRC) professionals are not looking at the implementation of such programmes, there are many similar GRC “Big data” challenges to be faced and potential lessons to be learned from these high profile government programmes that can be applied a lot closer to home. For example, how can GRC professionals collect, manage and analyze an enormous and disparate volume of data to create and manage their own actionable intelligence covering hidden signs and patterns of criminal activity, the early or retrospective, violation of regulations/laws/corporate policies and procedures, emerging risks and weakening controls etc. Not exactly the stuff of James Bond to be sure, but it is certainly more applicable to most GRC professional’s day to day challenges. So what is Big Data and how can it benefit the GRC process? Although it often varies, the definition of Big Data largely refers to the following types of data: Traditional Enterprise Data – includes customer information from CRM systems, transactional ERP data, web store transactions, and general ledger data. Machine-Generated /Sensor Data – includes Call Detail Records (“CDR”), weblogs and trading systems data. Social Data – includes customer feedback streams, micro-blogging sites like Twitter, and social media platforms like Facebook. The McKinsey Global Institute estimates that data volume is growing 40% per year, and will grow 44x between 2009 and 2020. But while it’s often the most visible parameter, volume of data is not the only characteristic that matters. In fact, according to sources such as Forrester there are four key characteristics that define big data: Volume. Machine-generated data is produced in much larger quantities than non-traditional data. This is all the data generated by IT systems that power the enterprise. This includes live data from packaged and custom applications – for example, app servers, Web servers, databases, networks, virtual machines, telecom equipment, and much more. Velocity. Social media data streams – while not as massive as machine-generated data – produce a large influx of opinions and relationships valuable to customer relationship management as well as offering early insight into potential reputational risk issues. Even at 140 characters per tweet, the high velocity (or frequency) of Twitter data ensures large volumes (over 8 TB per day) need to be managed. Variety. Traditional data formats tend to be relatively well defined by a data schema and change slowly. In contrast, non-traditional data formats exhibit a dizzying rate of change. Without question, all GRC professionals work in a dynamic environment and as new services, new products, new business lines are added or new marketing campaigns executed for example, new data types are needed to capture the resultant information.  Value. The economic value of data varies significantly. Typically, there is good information hidden amongst a larger body of non-traditional data that GRC professionals can use to add real value to the organisation; the greater challenge is identifying what is valuable and then transforming and extracting that data for analysis and action. For example, customer service calls and emails have millions of useful data points and have long been a source of information to GRC professionals. Those calls and emails are critical in helping GRC professionals better identify hidden patterns and implement new policies that can reduce the amount of customer complaints.   Now on a scale and depth far beyond those in place today, all that unstructured call and email data can be captured, stored and analyzed to reveal the reasons for the contact, perhaps with the aggregated customer results cross referenced against what is being said about the organization or a similar peer organization on social media. The organization can then take positive actions, communicating to the market in advance of issues reaching the press, strengthening controls, adjusting risk profiles, changing policy and procedures and completely minimizing, if not eliminating, complaints and compensation for that specific reason in the future. In this one example of many similar ones, the GRC team(s) has demonstrated real and tangible business value. Big Challenges - Big Opportunities As pointed out by recent Forrester research, high performing companies (those that are growing 15% or more year-on-year compared to their peers) are taking a selective approach to investing in Big Data.  "Tomorrow's winners understand this, and they are making selective investments aimed at specific opportunities with tangible benefits where big data offers a more economical solution to meet a need." (Forrsights Strategy Spotlight: Business Intelligence and Big Data, Q4 2012) As pointed out earlier, with the ever increasing volume of regulatory demands and fines for getting it wrong, limited resource availability and out of date or inadequate GRC systems all contributing to a higher cost of compliance and/or higher risk profile than desired – a big data investment in GRC clearly falls into this category. However, to make the most of big data organizations must evolve both their business and IT procedures, processes, people and infrastructures to handle these new high-volume, high-velocity, high-variety sources of data and be able integrate them with the pre-existing company data to be analyzed. GRC big data clearly allows the organization access to and management over a huge amount of often very sensitive information that although can help create a more risk intelligent organization, also presents numerous data governance challenges, including regulatory compliance and information security. In addition to client and regulatory demands over better information security and data protection the sheer amount of information organizations deal with the need to quickly access, classify, protect and manage that information can quickly become a key issue  from a legal, as well as technical or operational standpoint. However, by making information governance processes a bigger part of everyday operations, organizations can make sure data remains readily available and protected. The Right GRC & Big Data Partnership Becomes Key  The "getting it right first time" mantra used in so many companies remains essential for any GRC team that is sponsoring, helping kick start, or even overseeing a big data project. To make a big data GRC initiative work and get the desired value, partnerships with companies, who have a long history of success in delivering successful GRC solutions as well as being at the very forefront of technology innovation, becomes key. Clearly solutions can be built in-house more cheaply than through vendor, but as has been proven time and time again, when it comes to self built solutions covering AML and Fraud for example, few have able to scale or adapt appropriately to meet the changing regulations or challenges that the GRC teams face on a daily basis. This has led to the creation of GRC silo’s that are causing so many headaches today. The solutions that stand out and should be explored are the ones that can seamlessly merge the traditional world of well-known data, analytics and visualization with the new world of seemingly innumerable data sources, utilizing Big Data technologies to generate new GRC insights right across the enterprise.Ultimately, Big Data is here to stay, and organizations that embrace its potential and outline a viable strategy, as well as understand and build a solid analytical foundation, will be the ones that are well positioned to make the most of it. A Blueprint and Roadmap Service for Big Data Big data adoption is first and foremost a business decision. As such it is essential that your partner can align your strategies, goals, and objectives with an architecture vision and roadmap to accelerate adoption of big data for your environment, as well as establish practical, effective governance that will maintain a well managed environment going forward. Key Activities: While your initiatives will clearly vary, there are some generic starting points the team and organization will need to complete: Clearly define your drivers, strategies, goals, objectives and requirements as it relates to big data Conduct a big data readiness and Information Architecture maturity assessment Develop future state big data architecture, including views across all relevant architecture domains; business, applications, information, and technology Provide initial guidance on big data candidate selection for migrations or implementation Develop a strategic roadmap and implementation plan that reflects a prioritization of initiatives based on business impact and technology dependency, and an incremental integration approach for evolving your current state to the target future state in a manner that represents the least amount of risk and impact of change on the business Provide recommendations for practical, effective Data Governance, Data Quality Management, and Information Lifecycle Management to maintain a well-managed environment Conduct an executive workshop with recommendations and next steps There is little debate that managing risk and data are the two biggest obstacles encountered by financial institutions.  Big data is here to stay and risk management certainly is not going anywhere, and ultimately financial services industry organizations that embrace its potential and outline a viable strategy, as well as understand and build a solid analytical foundation, will be best positioned to make the most of it. Matthew Long is a Financial Crime Specialist for Oracle Financial Services. He can be reached at matthew.long AT oracle.com.

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• Introducing Data Annotations Extensions

  - by srkirkland

  Validation of user input is integral to building a modern web application, and ASP.NET MVC offers us a way to enforce business rules on both the client and server using Model Validation.  The recent release of ASP.NET MVC 3 has improved these offerings on the client side by introducing an unobtrusive validation library built on top of jquery.validation.  Out of the box MVC comes with support for Data Annotations (that is, System.ComponentModel.DataAnnotations) and can be extended to support other frameworks.  Data Annotations Validation is becoming more popular and is being baked in to many other Microsoft offerings, including Entity Framework, though with MVC it only contains four validators: Range, Required, StringLength and Regular Expression.  The Data Annotations Extensions project attempts to augment these validators with additional attributes while maintaining the clean integration Data Annotations provides. A Quick Word About Data Annotations Extensions The Data Annotations Extensions project can be found at http://dataannotationsextensions.org/, and currently provides 11 additional validation attributes (ex: Email, EqualTo, Min/Max) on top of Data Annotations’ original 4.  You can find a current list of the validation attributes on the afore mentioned website. The core library provides server-side validation attributes that can be used in any .NET 4.0 project (no MVC dependency). There is also an easily pluggable client-side validation library which can be used in ASP.NET MVC 3 projects using unobtrusive jquery validation (only MVC3 included javascript files are required). On to the Preview Let’s say you had the following “Customer” domain model (or view model, depending on your project structure) in an MVC 3 project: public class Customer { public string Email { get; set; } public int Age { get; set; } public string ProfilePictureLocation { get; set; } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } When it comes time to create/edit this Customer, you will probably have a CustomerController and a simple form that just uses one of the Html.EditorFor() methods that the ASP.NET MVC tooling generates for you (or you can write yourself).  It should look something like this: With no validation, the customer can enter nonsense for an email address, and then can even report their age as a negative number!  With the built-in Data Annotations validation, I could do a bit better by adding a Range to the age, adding a RegularExpression for email (yuck!), and adding some required attributes.  However, I’d still be able to report my age as 10.75 years old, and my profile picture could still be any string.  Let’s use Data Annotations along with this project, Data Annotations Extensions, and see what we can get: public class Customer { [Email] [Required] public string Email { get; set; }   [Integer] [Min(1, ErrorMessage="Unless you are benjamin button you are lying.")] [Required] public int Age { get; set; }   [FileExtensions("png|jpg|jpeg|gif")] public string ProfilePictureLocation { get; set; } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Now let’s try to put in some invalid values and see what happens: That is very nice validation, all done on the client side (will also be validated on the server).  Also, the Customer class validation attributes are very easy to read and understand. Another bonus: Since Data Annotations Extensions can integrate with MVC 3’s unobtrusive validation, no additional scripts are required! Now that we’ve seen our target, let’s take a look at how to get there within a new MVC 3 project. Adding Data Annotations Extensions To Your Project First we will File->New Project and create an ASP.NET MVC 3 project.  I am going to use Razor for these examples, but any view engine can be used in practice.  Now go into the NuGet Extension Manager (right click on references and select add Library Package Reference) and search for “DataAnnotationsExtensions.”  You should see the following two packages: The first package is for server-side validation scenarios, but since we are using MVC 3 and would like comprehensive sever and client validation support, click on the DataAnnotationsExtensions.MVC3 project and then click Install.  This will install the Data Annotations Extensions server and client validation DLLs along with David Ebbo’s web activator (which enables the validation attributes to be registered with MVC 3). Now that Data Annotations Extensions is installed you have all you need to start doing advanced model validation.  If you are already using Data Annotations in your project, just making use of the additional validation attributes will provide client and server validation automatically.  However, assuming you are starting with a blank project I’ll walk you through setting up a controller and model to test with. Creating Your Model In the Models folder, create a new User.cs file with a User class that you can use as a model.  To start with, I’ll use the following class: public class User { public string Email { get; set; } public string Password { get; set; } public string PasswordConfirm { get; set; } public string HomePage { get; set; } public int Age { get; set; } } Next, create a simple controller with at least a Create method, and then a matching Create view (note, you can do all of this via the MVC built-in tooling).  Your files will look something like this: UserController.cs: public class UserController : Controller { public ActionResult Create() { return View(new User()); }   [HttpPost] public ActionResult Create(User user) { if (!ModelState.IsValid) { return View(user); }   return Content("User valid!"); } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Create.cshtml: @model NuGetValidationTester.Models.User   @{ ViewBag.Title = "Create"; }   <h2>Create</h2>   <script src="@Url.Content("~/Scripts/jquery.validate.min.js")" type="text/javascript"></script> <script src="@Url.Content("~/Scripts/jquery.validate.unobtrusive.min.js")" type="text/javascript"></script>   @using (Html.BeginForm()) { @Html.ValidationSummary(true) <fieldset> <legend>User</legend> @Html.EditorForModel() <p> <input type="submit" value="Create" /> </p> </fieldset> } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } In the Create.cshtml view, note that we are referencing jquery validation and jquery unobtrusive (jquery is referenced in the layout page).  These MVC 3 included scripts are the only ones you need to enjoy both the basic Data Annotations validation as well as the validation additions available in Data Annotations Extensions.  These references are added by default when you use the MVC 3 “Add View” dialog on a modification template type. Now when we go to /User/Create we should see a form for editing a User Since we haven’t yet added any validation attributes, this form is valid as shown (including no password, email and an age of 0).  With the built-in Data Annotations attributes we can make some of the fields required, and we could use a range validator of maybe 1 to 110 on Age (of course we don’t want to leave out supercentenarians) but let’s go further and validate our input comprehensively using Data Annotations Extensions.  The new and improved User.cs model class. { [Required] [Email] public string Email { get; set; }   [Required] public string Password { get; set; }   [Required] [EqualTo("Password")] public string PasswordConfirm { get; set; }   [Url] public string HomePage { get; set; }   [Integer] [Min(1)] public int Age { get; set; } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Now let’s re-run our form and try to use some invalid values: All of the validation errors you see above occurred on the client, without ever even hitting submit.  The validation is also checked on the server, which is a good practice since client validation is easily bypassed. That’s all you need to do to start a new project and include Data Annotations Extensions, and of course you can integrate it into an existing project just as easily. Nitpickers Corner ASP.NET MVC 3 futures defines four new data annotations attributes which this project has as well: CreditCard, Email, Url and EqualTo.  Unfortunately referencing MVC 3 futures necessitates taking an dependency on MVC 3 in your model layer, which may be unadvisable in a multi-tiered project.  Data Annotations Extensions keeps the server and client side libraries separate so using the project’s validation attributes don’t require you to take any additional dependencies in your model layer which still allowing for the rich client validation experience if you are using MVC 3. Custom Error Message and Globalization: Since the Data Annotations Extensions are build on top of Data Annotations, you have the ability to define your own static error messages and even to use resource files for very customizable error messages. Available Validators: Please see the project site at http://dataannotationsextensions.org/ for an up-to-date list of the new validators included in this project.  As of this post, the following validators are available: CreditCard Date Digits Email EqualTo FileExtensions Integer Max Min Numeric Url Conclusion Hopefully I’ve illustrated how easy it is to add server and client validation to your MVC 3 projects, and how to easily you can extend the available validation options to meet real world needs. The Data Annotations Extensions project is fully open source under the BSD license.  Any feedback would be greatly appreciated.  More information than you require, along with links to the source code, is available at http://dataannotationsextensions.org/. Enjoy!

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• Observing flow control idle time in TCP

  - by user12820842

  Previously I described how to observe congestion control strategies during transmission, and here I talked about TCP's sliding window approach for handling flow control on the receive side. A neat trick would now be to put the pieces together and ask the following question - how often is TCP transmission blocked by congestion control (send-side flow control) versus a zero-sized send window (which is the receiver saying it cannot process any more data)? So in effect we are asking whether the size of the receive window of the peer or the congestion control strategy may be sub-optimal. The result of such a problem would be that we have TCP data that we could be transmitting but we are not, potentially effecting throughput. So flow control is in effect: when the congestion window is less than or equal to the amount of bytes outstanding on the connection. We can derive this from args[3]-tcps_snxt - args[3]-tcps_suna, i.e. the difference between the next sequence number to send and the lowest unacknowledged sequence number; and when the window in the TCP segment received is advertised as 0 We time from these events until we send new data (i.e. args[4]-tcp_seq = snxt value when window closes. Here's the script: #!/usr/sbin/dtrace -s #pragma D option quiet tcp:::send / (args[3]-tcps_snxt - args[3]-tcps_suna) = args[3]-tcps_cwnd / { cwndclosed[args[1]-cs_cid] = timestamp; cwndsnxt[args[1]-cs_cid] = args[3]-tcps_snxt; @numclosed["cwnd", args[2]-ip_daddr, args[4]-tcp_dport] = count(); } tcp:::send / cwndclosed[args[1]-cs_cid] && args[4]-tcp_seq = cwndsnxt[args[1]-cs_cid] / { @meantimeclosed["cwnd", args[2]-ip_daddr, args[4]-tcp_dport] = avg(timestamp - cwndclosed[args[1]-cs_cid]); @stddevtimeclosed["cwnd", args[2]-ip_daddr, args[4]-tcp_dport] = stddev(timestamp - cwndclosed[args[1]-cs_cid]); @numclosed["cwnd", args[2]-ip_daddr, args[4]-tcp_dport] = count(); cwndclosed[args[1]-cs_cid] = 0; cwndsnxt[args[1]-cs_cid] = 0; } tcp:::receive / args[4]-tcp_window == 0 && (args[4]-tcp_flags & (TH_SYN|TH_RST|TH_FIN)) == 0 / { swndclosed[args[1]-cs_cid] = timestamp; swndsnxt[args[1]-cs_cid] = args[3]-tcps_snxt; @numclosed["swnd", args[2]-ip_saddr, args[4]-tcp_dport] = count(); } tcp:::send / swndclosed[args[1]-cs_cid] && args[4]-tcp_seq = swndsnxt[args[1]-cs_cid] / { @meantimeclosed["swnd", args[2]-ip_daddr, args[4]-tcp_sport] = avg(timestamp - swndclosed[args[1]-cs_cid]); @stddevtimeclosed["swnd", args[2]-ip_daddr, args[4]-tcp_sport] = stddev(timestamp - swndclosed[args[1]-cs_cid]); swndclosed[args[1]-cs_cid] = 0; swndsnxt[args[1]-cs_cid] = 0; } END { printf("%-6s %-20s %-8s %-25s %-8s %-8s\n", "Window", "Remote host", "Port", "TCP Avg WndClosed(ns)", "StdDev", "Num"); printa("%-6s %-20s %-8d %@-25d %@-8d %@-8d\n", @meantimeclosed, @stddevtimeclosed, @numclosed); } So this script will show us whether the peer's receive window size is preventing flow ("swnd" events) or whether congestion control is limiting flow ("cwnd" events). As an example I traced on a server with a large file transfer in progress via a webserver and with an active ssh connection running "find / -depth -print". Here is the output: ^C Window Remote host Port TCP Avg WndClosed(ns) StdDev Num cwnd 10.175.96.92 80 86064329 77311705 125 cwnd 10.175.96.92 22 122068522 151039669 81 So we see in this case, the congestion window closes 125 times for port 80 connections and 81 times for ssh. The average time the window is closed is 0.086sec for port 80 and 0.12sec for port 22. So if you wish to change congestion control algorithm in Oracle Solaris 11, a useful step may be to see if congestion really is an issue on your network. Scripts like the one posted above can help assess this, but it's worth reiterating that if congestion control is occuring, that's not necessarily a problem that needs fixing. Recall that congestion control is about controlling flow to prevent large-scale drops, so looking at congestion events in isolation doesn't tell us the whole story. For example, are we seeing more congestion events with one control algorithm, but more drops/retransmission with another? As always, it's best to start with measures of throughput and latency before arriving at a specific hypothesis such as "my congestion control algorithm is sub-optimal".

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• Cleaning a dataset of song data - what sort of problem is this?

  - by Rob Lourens

  I have a set of data about songs. Each entry is a line of text which includes the artist name, song title, and some extra text. Some entries are only "extra text". My goal is to resolve as many of these as possible to songs on Spotify using their web API. My strategy so far has been to search for the entry via the API - if there are no results, apply a transformation such as "remove all text between ( )" and search again. I have a list of heuristics and I've had reasonable success with this but as the code gets more and more convoluted I keep thinking there must be a more generic and consistent way. I don't know where to look - any suggestions for what to try, topics to study, buzzwords to google?

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• Augmenting your Social Efforts via Data as a Service (DaaS)

  - by Mike Stiles

  The following is the 3rd in a series of posts on the value of leveraging social data across your enterprise by Oracle VP Product Development Don Springer and Oracle Cloud Data and Insight Service Sr. Director Product Management Niraj Deo. In this post, we will discuss the approach and value of integrating additional “public” data via a cloud-based Data-as-as-Service platform (or DaaS) to augment your Socially Enabled Big Data Analytics and CX Management. Let’s assume you have a functional Social-CRM platform in place. You are now successfully and continuously listening and learning from your customers and key constituents in Social Media, you are identifying relevant posts and following up with direct engagement where warranted (both 1:1, 1:community, 1:all), and you are starting to integrate signals for communication into your appropriate Customer Experience (CX) Management systems as well as insights for analysis in your business intelligence application. What is the next step? Augmenting Social Data with other Public Data for More Advanced Analytics When we say advanced analytics, we are talking about understanding causality and correlation from a wide variety, volume and velocity of data to Key Performance Indicators (KPI) to achieve and optimize business value. And in some cases, to predict future performance to make appropriate course corrections and change the outcome to your advantage while you can. The data to acquire, process and analyze this is very nuanced: It can vary across structured, semi-structured, and unstructured data It can span across content, profile, and communities of profiles data It is increasingly public, curated and user generated The key is not just getting the data, but making it value-added data and using it to help discover the insights to connect to and improve your KPIs. As we spend time working with our larger customers on advanced analytics, we have seen a need arise for more business applications to have the ability to ingest and use “quality” curated, social, transactional reference data and corresponding insights. The challenge for the enterprise has been getting this data inline into an easily accessible system and providing the contextual integration of the underlying data enriched with insights to be exported into the enterprise’s business applications. The following diagram shows the requirements for this next generation data and insights service or (DaaS): Some quick points on these requirements: Public Data, which in this context is about Common Business Entities, such as - Customers, Suppliers, Partners, Competitors (all are organizations) Contacts, Consumers, Employees (all are people) Products, Brands This data can be broadly categorized incrementally as - Base Utility data (address, industry classification) Public Master Reference data (trade style, hierarchy) Social/Web data (News, Feeds, Graph) Transactional Data generated by enterprise process, workflows etc. This Data has traits of high-volume, variety, velocity etc., and the technology needed to efficiently integrate this data for your needs includes - Change management of Public Reference Data across all categories Applied Big Data to extract statics as well as real-time insights Knowledge Diagnostics and Data Mining As you consider how to deploy this solution, many of our customers will be using an online “cloud” service that provides quality data and insights uniformly to all their necessary applications. In addition, they are requesting a service that is: Agile and Easy to Use: Applications integrated with the service can obtain data on-demand, quickly and simply Cost-effective: Pre-integrated into applications so customers don’t have to Has High Data Quality: Single point access to reference data for data quality and linkages to transactional, curated and social data Supports Data Governance: Becomes more manageable and cost-effective since control of data privacy and compliance can be enforced in a centralized place Data-as-a-Service (DaaS) Just as the cloud has transformed and now offers a better path for how an enterprise manages its IT from their infrastructure, platform, and software (IaaS, PaaS, and SaaS), the next step is data (DaaS). Over the last 3 years, we have seen the market begin to offer a cloud-based data service and gain initial traction. On one side of the DaaS continuum, we see an “appliance” type of service that provides a single, reliable source of accurate business data plus social information about accounts, leads, contacts, etc. On the other side of the continuum we see more of an online market “exchange” approach where ISVs and Data Publishers can publish and sell premium datasets within the exchange, with the exchange providing a rich set of web interfaces to improve the ease of data integration. Why the difference? It depends on the provider’s philosophy on how fast the rate of commoditization of certain data types will occur. How do you decide the best approach? Our perspective, as shown in the diagram below, is that the enterprise should develop an elastic schema to support multi-domain applicability. This allows the enterprise to take the most flexible approach to harness the speed and breadth of public data to achieve value. The key tenet of the proposed approach is that an enterprise carefully federates common utility, master reference data end points, mobility considerations and content processing, so that they are pervasively available. One way you may already be familiar with this approach is in how you do Address Verification treatments for accounts, contacts etc. If you design and revise this service in such a way that it is also easily available to social analytic needs, you could extend this to launch geo-location based social use cases (marketing, sales etc.). Our fundamental belief is that value-added data achieved through enrichment with specialized algorithms, as well as applying business “know-how” to weight-factor KPIs based on innovative combinations across an ever-increasing variety, volume and velocity of data, will be where real value is achieved. Essentially, Data-as-a-Service becomes a single entry point for the ever-increasing richness and volume of public data, with enrichment and combined capabilities to extract and integrate the right data from the right sources with the right factoring at the right time for faster decision-making and action within your core business applications. As more data becomes available (and in many cases commoditized), this value-added data processing approach will provide you with ongoing competitive advantage. Let’s look at a quick example of creating a master reference relationship that could be used as an input for a variety of your already existing business applications. In phase 1, a simple master relationship is achieved between a company (e.g. General Motors) and a variety of car brands’ social insights. The reference data allows for easy sort, export and integration into a set of CRM use cases for analytics, sales and marketing CRM. In phase 2, as you create more data relationships (e.g. competitors, contacts, other brands) to have broader and deeper references (social profiles, social meta-data) for more use cases across CRM, HCM, SRM, etc. This is just the tip of the iceberg, as the amount of master reference relationships is constrained only by your imagination and the availability of quality curated data you have to work with. DaaS is just now emerging onto the marketplace as the next step in cloud transformation. For some of you, this may be the first you have heard about it. Let us know if you have questions, or perspectives. In the meantime, we will continue to share insights as we can.Photo: Erik Araujo, stock.xchng

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• Working with data and meta data that are separated on different servers

  - by afuzzyllama

  While developing a product, I've come across a situation where my group wants to store meta data for data entry forms (questions, layout, etc) in a different database then the database where the collected data is stored. This is mostly for security because we want to be able to have our meta data public facing, while keeping collected data as secure as possible. I was thinking about writing a web service that provides the meta information that the data collection program could access. The only issue I see with this approach is the front end is going to have to match the meta data with the collected data, which would be more efficient as a join on the back end. Currently, this system is slated to run on .NET and MSSQL. I haven't played around with .NET libraries running in SQL, but I'm considering trying to create logic that would pull from the web service, convert the meta data into a table that SQL can join on, and return the combined data and meta data that way. Is this solution the wrong way to approach the problem? Is there a pattern or "industry standard" way of bringing together two datasets that don't live in the same database?

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• The Business case for Big Data

  - by jasonw

  The Business Case for Big Data Part 1 What's the Big Deal Okay, so a new buzz word is emerging. It's gone beyond just a buzzword now, and I think it is going to change the landscape of retail, financial services, healthcare....everything. Let me spend a moment to talk about what i'm going to talk about. Massive amounts of data are being collected every second, more than ever imaginable, and the size of this data is more than can be practically managed by today’s current strategies and technologies. There is a revolution at hand centering on this groundswell of data and it will change how we execute our businesses through greater efficiencies, new revenue discovery and even enable innovation. It is the revolution of Big Data. This is more than just a new buzzword is being tossed around technology circles.This blog series for Big Data will explain this new wave of technology and provide a roadmap for businesses to take advantage of this growing trend. Cases for Big Data There is a growing list of use cases for big data. We naturally think of Marketing as the low hanging fruit. Many projects look to analyze twitter feeds to find new ways to do marketing. I think of a great example from a TED speech that I recently saw on data visualization from Facebook from my masters studies at University of Virginia. We can see when the most likely time for breaks-ups occurs by looking at status changes and updates on users Walls. This is the intersection of Big Data, Analytics and traditional structured data. Ted Video Marketers can use this to sell more stuff. I really like the following piece on looking at twitter feeds to measure mood. The following company was bought by a hedge fund. They could predict how the S&P was going to do within three days at an 85% accuracy. Link to the article Here we see a convergence of predictive analytics and Big Data. So, we'll look at a lot of these business cases and start talking about what this means for the business. It's more than just finding ways to use Hadoop + NoSql and we'll talk about that too. How do I start in Big Data? That's what is coming next post.

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• Big Data – Buzz Words: What is NoSQL – Day 5 of 21

  - by Pinal Dave

  In yesterday’s blog post we explored the basic architecture of Big Data . In this article we will take a quick look at one of the four most important buzz words which goes around Big Data – NoSQL. What is NoSQL? NoSQL stands for Not Relational SQL or Not Only SQL. Lots of people think that NoSQL means there is No SQL, which is not true – they both sound same but the meaning is totally different. NoSQL does use SQL but it uses more than SQL to achieve its goal. As per Wikipedia’s NoSQL Database Definition – “A NoSQL database provides a mechanism for storage and retrieval of data that uses looser consistency models than traditional relational databases.“ Why use NoSQL? A traditional relation database usually deals with predictable structured data. Whereas as the world has moved forward with unstructured data we often see the limitations of the traditional relational database in dealing with them. For example, nowadays we have data in format of SMS, wave files, photos and video format. It is a bit difficult to manage them by using a traditional relational database. I often see people using BLOB filed to store such a data. BLOB can store the data but when we have to retrieve them or even process them the same BLOB is extremely slow in processing the unstructured data. A NoSQL database is the type of database that can handle unstructured, unorganized and unpredictable data that our business needs it. Along with the support to unstructured data, the other advantage of NoSQL Database is high performance and high availability. Eventual Consistency Additionally to note that NoSQL Database may not provided 100% ACID (Atomicity, Consistency, Isolation, Durability) compliance.  Though, NoSQL Database does not support ACID they provide eventual consistency. That means over the long period of time all updates can be expected to propagate eventually through the system and data will be consistent. Taxonomy Taxonomy is the practice of classification of things or concepts and the principles. The NoSQL taxonomy supports column store, document store, key-value stores, and graph databases. We will discuss the taxonomy in detail in later blog posts. Here are few of the examples of the each of the No SQL Category. Column: Hbase, Cassandra, Accumulo Document: MongoDB, Couchbase, Raven Key-value : Dynamo, Riak, Azure, Redis, Cache, GT.m Graph: Neo4J, Allegro, Virtuoso, Bigdata As of now there are over 150 NoSQL Database and you can read everything about them in this single link. Tomorrow In tomorrow’s blog post we will discuss Buzz Word – Hadoop. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• C++ Program Flow: Sockets in an Object and the Main Function

  - by jfm429

  I have a rather tricky problem regarding C++ program flow using sockets. Basically what I have is this: a simple command-line socket server program that listens on a socket and accepts one connection at a time. When that connection is lost it opens up for further connections. That socket communication system is contained in a class. The class is fully capable of receiving the connections and mirroring the data received to the client. However, the class uses UNIX sockets, which are not object-oriented. My problem is that in my main() function, I have one line - the one that creates an instance of that object. The object then initializes and waits. But as soon as a connection is gained, the object's initialization function returns, and when that happens, the program quits. How do I somehow wait until this object is deleted before the program quits? Summary: main() creates instance of object Object listens Connection received Object's initialization function returns main() exits (!) What I want is for main() to somehow delay until that object is finished with what it's doing (aka it will delete itself) before it quits. Any thoughts?

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• Recover data loss from accidental quick format

  - by Bo Tian

  I've accidentally performed a quick format on the mobile hard disk thinking that the data is useless. Now my mum says that she needs them back. What are the tools that I could use to recover the data?

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• New Feature in ODI 11.1.1.6: ODI for Big Data

  - by Julien Testut

  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: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:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";} By Ananth Tirupattur Starting with Oracle Data Integrator 11.1.1.6.0, ODI is offering a solution to process Big Data. This post provides an overview of this feature. With all the buzz around Big Data and before getting into the details of ODI for Big Data, I will provide a brief introduction to Big Data and Oracle Solution for Big Data. So, what is Big Data? Big data includes: structured data (this includes data from relation data stores, xml data stores), semi-structured data (this includes data from weblogs) unstructured data (this includes data from text blob, images) Traditionally, business decisions are based on the information gathered from transactional data. For example, transactional Data from CRM applications is fed to a decision system for analysis and decision making. Products such as ODI play a key role in enabling decision systems. However, with the emergence of massive amounts of semi-structured and unstructured data it is important for decision system to include them in the analysis to achieve better decision making capability. While there is an abundance of opportunities for business for gaining competitive advantages, process of Big Data has challenges. The challenges of processing Big Data include: Volume of data Velocity of data - The high Rate at which data is generated Variety of data In order to address these challenges and convert them into opportunities, we would need an appropriate framework, platform and the right set of tools. Hadoop is an open source framework which is highly scalable, fault tolerant system, for storage and processing large amounts of data. Hadoop provides 2 key services, distributed and reliable storage called Hadoop Distributed File System or HDFS and a framework for parallel data processing called Map-Reduce. Innovations in Hadoop and its related technology continue to rapidly evolve, hence therefore, it is highly recommended to follow information on the web to keep up with latest information. Oracle's vision is to provide a comprehensive solution to address the challenges faced by Big Data. Oracle is providing the necessary Hardware, software and tools for processing Big Data Oracle solution includes: Big Data Appliance Oracle NoSQL Database Cloudera distribution for Hadoop Oracle R Enterprise- R is a statistical package which is very popular among data scientists. ODI solution for Big Data Oracle Loader for Hadoop for loading data from Hadoop to Oracle. Further details can be found here: http://www.oracle.com/us/products/database/big-data-appliance/overview/index.html ODI Solution for Big Data: ODI’s goal is to minimize the need to understand the complexity of Hadoop framework and simplify the adoption of processing Big Data seamlessly in an enterprise. ODI is providing the capabilities for an integrated architecture for processing Big Data. This includes capability to load data in to Hadoop, process data in Hadoop and load data from Hadoop into Oracle. ODI is expanding its support for Big Data by providing the following out of the box Knowledge Modules (KMs). IKM File to Hive (LOAD DATA).Load unstructured data from File (Local file system or HDFS ) into Hive IKM Hive Control AppendTransform and validate structured data on Hive IKM Hive TransformTransform unstructured data on Hive IKM File/Hive to Oracle (OLH)Load processed data in Hive to Oracle RKM HiveReverse engineer Hive tables to generate models Using the Loading KM you can map files (local and HDFS files) to the corresponding Hive tables. For example, you can map weblog files categorized by date into a corresponding partitioned Hive table schema. 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: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:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";} Using the Hive control Append KM you can validate and transform data in Hive. In the below example, two source Hive tables are joined and mapped to a target Hive table. 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: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:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";} The Hive Transform KM facilitates processing of semi-structured data in Hive. In the below example, the data from weblog is processed using a Perl script and mapped to target Hive table. 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: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:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";} Using the Oracle Loader for Hadoop (OLH) KM you can load data from Hive table or HDFS to a corresponding table in Oracle. OLH is available as a standalone product. ODI greatly enhances OLH capability by generating the configuration and mapping files for OLH based on the configuration provided in the interface and KM options. ODI seamlessly invokes OLH when executing the scenario. In the below example, a HDFS file is mapped to a table in Oracle. Development and Deployment:The following diagram illustrates the development and deployment of ODI solution for Big Data. Using the ODI Studio on your development machine create and develop ODI solution for processing Big Data by connecting to a MySQL DB or Oracle database on a BDA machine or Hadoop cluster. Schedule the ODI scenarios to be executed on the ODI agent deployed on the BDA machine or Hadoop cluster. ODI Solution for Big Data provides several exciting new capabilities to facilitate the adoption of Big Data in an enterprise. You can find more information about the Oracle Big Data connectors on OTN. You can find an overview of all the new features introduced in ODI 11.1.1.6 in the following document: ODI 11.1.1.6 New Features Overview

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• Data recovery on a data HDD (no OS)

  - by aCuria

  I am helping a family member with a dead hard disk. It is a seagate 200Gb 3.5" HDD in one of those old-school external enclosures. The problem was that windows failed to detect the hard disk when plugged in through USB. I removed the hard disk from its enclosure, and plugged it into my desktop PC. The BIOS does detect it upon POST, but unfortunately windows 7 would refuse to boot. It will get stuck on the loading screen with the glowing windows logo. Safe mode doesn't help either. What options do I have before going for some professional data recovery? edit: Someone modified the Title to something completely different from what I was asking, i just changed it back. 1) 2 HDD drives, DiskA(Dead), DiskB(my OS disk) 2) when B is connected to my system, everything works fine 3) when A AND B is connected, failure to boot. POSTs fine, but windows wont load 4) A has NO OS, its PURE data. It came from an EXTERNAL HDD enclosure which doesnt belong to me, and im trying to do data recovery.

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• Data Governance 2010 Conference in San Diego

  - by Tony Ouk

  The Data Governance Annual Conference is one of the world's most authoritative and vendor neutral event on Data Governance and Data Quality.  The conference will focus on the "how-tos" from starting a data governance and stewardship program to attaining data governance maturity with specific topics on MDM.  This year's event will be hosted June 7 through June 10 in San Diego, California. For more information, including registration details, visit the Data Governance 2010 Conference website.

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• How to search for newline or linebreak characters in Excel?

  - by Highly Irregular

  I've imported some data into Excel (from a text file) and it contains some sort of newline characters. It looks like this initially: If I hit F2 (to edit) then Enter (to save changes) on each of the cells with a newline (without actually editing anything), Excel automatically changes the layout to look like this: I don't want these newlines characters here, as it messes up data processing further down the track. How can I do a search for these to detect more of them? The usual search function doesn't accept an enter character as a search character.

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• Oracle Data Integration 12c: Simplified, Future-Ready, High-Performance Solutions

  - by Thanos Terentes Printzios

  In today’s data-driven business environment, organizations need to cost-effectively manage the ever-growing streams of information originating both inside and outside the firewall and address emerging deployment styles like cloud, big data analytics, and real-time replication. Oracle Data Integration delivers pervasive and continuous access to timely and trusted data across heterogeneous systems. Oracle is enhancing its data integration offering announcing the general availability of 12c release for the key data integration products: Oracle Data Integrator 12c and Oracle GoldenGate 12c, delivering Simplified and High-Performance Solutions for Cloud, Big Data Analytics, and Real-Time Replication. 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 : Supporting Current and Emerging Initiatives Extreme Performance : Even higher performance than ever before Fast Time-to-Value : Higher IT Productivity and Simplified Solutions  With the new capabilities in Oracle Data Integrator 12c, customers can benefit from: Superior developer productivity, ease of use, and rapid time-to-market with the new flow-based mapping model, reusable mappings, and step-by-step debugger. Increased performance when executing data integration processes due to improved parallelism. Improved productivity and monitoring via tighter integration with Oracle GoldenGate 12c and Oracle Enterprise Manager 12c. Improved interoperability with Oracle Warehouse Builder which enables faster and easier migration to Oracle Data Integrator’s strategic data integration offering. Faster implementation of business analytics through Oracle Data Integrator pre-integrated with Oracle BI Applications’ latest release. Oracle Data Integrator also integrates simply and easily with Oracle Business Analytics tools, including OBI-EE and Oracle Hyperion. Support for loading and transforming big and fast data, enabled by integration with big data technologies: Hadoop, Hive, HDFS, and Oracle Big Data Appliance. Only Oracle GoldenGate provides the best-of-breed real-time replication of data in heterogeneous data environments. With the new capabilities in Oracle GoldenGate 12c, customers can benefit from: Simplified setup and management of Oracle GoldenGate 12c when using multiple database delivery processes via a new Coordinated Delivery feature for non-Oracle databases. Expanded heterogeneity through added support for the latest versions of major databases such as Sybase ASE v 15.7, MySQL NDB Clusters 7.2, and MySQL 5.6., as well as integration with Oracle Coherence. Enhanced high availability and data protection via integration with Oracle Data Guard and Fast-Start Failover integration. Enhanced security for credentials and encryption keys using Oracle Wallet. Real-time replication for databases hosted on public cloud environments supported by third-party clouds. Tight integration between Oracle Data Integrator 12c and Oracle GoldenGate 12c and other Oracle technologies, such as Oracle Database 12c and Oracle Applications, provides a number of benefits for organizations: 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. Integration with Oracle Database 12c provides a strong foundation for seamless private cloud deployments. Delivers real-time data for reporting, zero downtime migration, and improved performance and availability for Oracle Applications, such as Oracle E-Business Suite and ATG Web Commerce . Oracle’s data integration offering is optimized for Oracle Engineered Systems and is an integral part of Oracle’s fast data, real-time analytics strategy on Oracle Exadata Database Machine and Oracle Exalytics In-Memory Machine. Oracle Data Integrator 12c and Oracle GoldenGate 12c differentiate the new offering on data integration with these many new features. This is just a quick glimpse into Oracle Data Integrator 12c and Oracle GoldenGate 12c. Find out much more about the new release in the video webcast "Introducing 12c for Oracle Data Integration", where customer and partner speakers, including SolarWorld, BT, Rittman Mead will join us in launching the new release. Resource Kits Meet Oracle Data Integration 12c  Discover what's new with Oracle Goldengate 12c  Oracle EMEA DIS (Data Integration Solutions) Partner Community is available for all your questions, while additional partner focused webcasts will be made available through our blog here, so stay connected. For any questions please contact us at partner.imc-AT-beehiveonline.oracle-DOT-com Stay Connected Oracle Newsletters

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• Building a Data Mart with Pentaho Data Integration Video Review by Diethard Steiner, Packt Publishing

  - by Compudicted

  Originally posted on: http://geekswithblogs.net/Compudicted/archive/2014/06/01/building-a-data-mart-with-pentaho-data-integration-video-review.aspx The Building a Data Mart with Pentaho Data Integration Video by Diethard Steiner from Packt Publishing is more than just a course on how to use Pentaho Data Integration, it also implements and uses the principals of the Data Warehousing (and I even heard the name of Ralph Kimball in the video). Indeed, a video watcher should be familiar with its concepts as the Star Schema, Slowly Changing Dimension types, etc. so I suggest prior to watching this course to consider skimming through the Data Warehouse concepts (if unfamiliar) or even better, read the excellent Ralph’s The Data Warehouse Tooolkit. By the way, the author expands beyond using Pentaho along to MySQL and MonetDB which is a real icing on the cake! Indeed, I even suggest the name of the course should be ‘Building a Data Warehouse with Pentaho’. To successfully complete the course one needs to know some Linux (Ubuntu used in the course), the VI editor and the Bash command shell, but it seems that similar requirements would also apply to the Weindows OS. Additionally, knowing some basic SQL would not hurt. As I had said, MonetDB is used in this course several times which seems to be not anymore complex than say MySQL, but based on what I read is very well suited for fast querying big volumes of data thanks to having a columnstore (vertical data storage). I don’t see what else can be a barrier, the material is very digestible. On this note, I must add that the author does not cover how to acquire the software, so here is what I found may help: Pentaho: the free Community Edition must be more than anyone needs to learn it. Or even go into a POC. MonetDB can be downloaded (exists for both, Linux and Windows) from http://goo.gl/FYxMy0 (just see the appropriate link on the left). The author seems to be using Eclipse to run SQL code, one can get it from http://goo.gl/5CcuN. To create, or edit database entities and/or schema otherwise one can use a universal tool called SQuirreL, get it from http://squirrel-sql.sourceforge.net.   Next, I must confess Diethard is very knowledgeable in what he does and beyond. However, there will be some accent heard to the user of the course especially if one’s mother tongue language is English, but it I got over it in a few chapters. I liked the rate at which the material is being presented, it makes me feel I paid for every second Eventually, my impressions are: Pentaho is an awesome ETL offering, it is worth learning it very much (I am an ETL fan and a heavy user of SSIS) MonetDB is nice, it tickles my fancy to know it more Data Warehousing, despite all the BigData tool offerings (Hive, Scoop, Pig on Hadoop), using the traditional tools still rocks Chapters 2 to 6 were the most fun to me with chapter 8 being the most difficult.   In terms of closing, I highly recommend this video to anyone who needs to grasp Pentaho concepts quick, likewise, the course is very well suited for any developer on a “supposed to be done yesterday” type of a project. It is for a beginner to intermediate level ETL/DW developer. But one would need to learn more on Data Warehousing and Pentaho, for such I recommend the 5 star Pentaho Data Integration 4 Cookbook. Enjoy it! Disclaimer: I received this video from the publisher for the purpose of a public review.

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• Building a Data Mart with Pentaho Data Integration Video Review by Diethard Steiner, Packt Publishing

  - by Compudicted

  Originally posted on: http://geekswithblogs.net/Compudicted/archive/2014/06/01/building-a-data-mart-with-pentaho-data-integration-video-review-again.aspx The Building a Data Mart with Pentaho Data Integration Video by Diethard Steiner from Packt Publishing is more than just a course on how to use Pentaho Data Integration, it also implements and uses the principals of the Data Warehousing (and I even heard the name of Ralph Kimball in the video). Indeed, a video watcher should be familiar with its concepts as the Star Schema, Slowly Changing Dimension types, etc. so I suggest prior to watching this course to consider skimming through the Data Warehouse concepts (if unfamiliar) or even better, read the excellent Ralph’s The Data Warehouse Tooolkit. By the way, the author expands beyond using Pentaho along to MySQL and MonetDB which is a real icing on the cake! Indeed, I even suggest the name of the course should be ‘Building a Data Warehouse with Pentaho’. To successfully complete the course one needs to know some Linux (Ubuntu used in the course), the VI editor and the Bash command shell, but it seems that similar requirements would also apply to the Windows OS. Additionally, knowing some basic SQL would not hurt. As I had said, MonetDB is used in this course several times which seems to be not anymore complex than say MySQL, but based on what I read is very well suited for fast querying big volumes of data thanks to having a columnstore (vertical data storage). I don’t see what else can be a barrier, the material is very digestible. On this note, I must add that the author does not cover how to acquire the software, so here is what I found may help: Pentaho: the free Community Edition must be more than anyone needs to learn it. Or even go into a POC. MonetDB can be downloaded (exists for both, Linux and Windows) from http://goo.gl/FYxMy0 (just see the appropriate link on the left). The author seems to be using Eclipse to run SQL code, one can get it from http://goo.gl/5CcuN. To create, or edit database entities and/or schema otherwise one can use a universal tool called SQuirreL, get it from http://squirrel-sql.sourceforge.net.   Next, I must confess Diethard is very knowledgeable in what he does and beyond. However, there will be some accent heard to the user of the course especially if one’s mother tongue language is English, but it I got over it in a few chapters. I liked the rate at which the material is being presented, it makes me feel I paid for every second Eventually, my impressions are: Pentaho is an awesome ETL offering, it is worth learning it very much (I am an ETL fan and a heavy user of SSIS) MonetDB is nice, it tickles my fancy to know it more Data Warehousing, despite all the BigData tool offerings (Hive, Scoop, Pig on Hadoop), using the traditional tools still rocks Chapters 2 to 6 were the most fun to me with chapter 8 being the most difficult.   In terms of closing, I highly recommend this video to anyone who needs to grasp Pentaho concepts quick, likewise, the course is very well suited for any developer on a “supposed to be done yesterday” type of a project. It is for a beginner to intermediate level ETL/DW developer. But one would need to learn more on Data Warehousing and Pentaho, for such I recommend the 5 star Pentaho Data Integration 4 Cookbook. Enjoy it! Disclaimer: I received this video from the publisher for the purpose of a public review.

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• Internal Mutation of Persistent Data Structures

  - by Greg Ros

  To clarify, when I mean use the terms persistent and immutable on a data structure, I mean that: The state of the data structure remains unchanged for its lifetime. It always holds the same data, and the same operations always produce the same results. The data structure allows Add, Remove, and similar methods that return new objects of its kind, modified as instructed, that may or may not share some of the data of the original object. However, while a data structure may seem to the user as persistent, it may do other things under the hood. To be sure, all data structures are, internally, at least somewhere, based on mutable storage. If I were to base a persistent vector on an array, and copy it whenever Add is invoked, it would still be persistent, as long as I modify only locally created arrays. However, sometimes, you can greatly increase performance by mutating a data structure under the hood. In more, say, insidious, dangerous, and destructive ways. Ways that might leave the abstraction untouched, not letting the user know anything has changed about the data structure, but being critical in the implementation level. For example, let's say that we have a class called ArrayVector implemented using an array. Whenever you invoke Add, you get a ArrayVector build on top of a newly allocated array that has an additional item. A sequence of such updates will involve n array copies and allocations. Here is an illustration: However, let's say we implement a lazy mechanism that stores all sorts of updates -- such as Add, Set, and others in a queue. In this case, each update requires constant time (adding an item to a queue), and no array allocation is involved. When a user tries to get an item in the array, all the queued modifications are applied under the hood, requiring a single array allocation and copy (since we know exactly what data the final array will hold, and how big it will be). Future get operations will be performed on an empty cache, so they will take a single operation. But in order to implement this, we need to 'switch' or mutate the internal array to the new one, and empty the cache -- a very dangerous action. However, considering that in many circumstances (most updates are going to occur in sequence, after all), this can save a lot of time and memory, it might be worth it -- you will need to ensure exclusive access to the internal state, of course. This isn't a question about the efficacy of such a data structure. It's a more general question. Is it ever acceptable to mutate the internal state of a supposedly persistent or immutable object in destructive and dangerous ways? Does performance justify it? Would you still be able to call it immutable? Oh, and could you implement this sort of laziness without mutating the data structure in the specified fashion?

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• Sabre Manages Fast Data Growth with Oracle Data Integration Products

  - by Irem Radzik

  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-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;} Last year at OpenWorld we announced Sabre Holding as a winner of the Fusion Middleware Innovation Awards. The Sabre team did an excellent job at leveraging cutting edge technologies for managing rapid data growth and exponential scalability demands they have experienced in the travel industry. Today we announced the details and specific benefits of Sabre’s new real-time data integration solution in a press release. Please take a look if you haven’t seen it yet. Sabre Holdings Deploys Oracle Data Integrator and Oracle GoldenGate to Support Rapid Customer Growth There are 3 different areas of benefits Sabre achieved by using Oracle Data Integration products: Manages 7X increase in data sources for the enterprise data warehouse Reduced infrastructure complexity Decreased time to market for new products and services by 30 percent. This simply shows that using latest technologies helps the companies to innovate robust solutions against today’s key data management challenges. And the benefit of using a next generation data integration technology is not only seen in the IT operations, but also in the business side. A better data integration solution for the enterprise data warehouse delivered the platform they need to accelerate how they service their customers, improving their competitive advantage. Tomorrow I will give another great example of innovation with next generation data integration from Oracle. We will be discussing the Fusion Middleware Innovation Awards 2012 winners and their results with using Oracle’s data integration products.

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