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  • F5 Big-IP iRule - HTTP Redirect

    - by djo
    I have just started to work with F5's Big-IP and I have a question about iRules and HTTP redirects. We are moving to offload our SSL from our web servers and onto the F5, our application as it stands enforces a number of pages on our site to only run in HTTPS. I want to move this from the APP and onto the F5 but I have not been able to figure our how, so as an example I would want anyone trying to login in to be forced to use HTTPS e.g. http://"mysite"/login.aspx = https://"mysite"/login.aspx. I have done some google searches that have come up with some good info on this but I have yet to find what I am looking for, if anyone has done this and wishes to share this with me that would be great

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  • Big IP F5 outbound HTTP issues

    - by mbuk2k
    We've tried upgrading from 9.x to 10.2 on our F5 Big IP 3400 and everything went over fine apart from one thing. We're unable to establish any outbound HTTP (80) connections from any servers that are assigned to a virtual server. This is something that worked before and is required for certain calls our servers need to make. Interestingly HTTPS (443) connections work fine, it's literally just anything outbound over port 80 seems to fail. Does anyone know if anything has changed between 9.4 and 10.2 that would mean additional config would need to be made to allow for external HTTP connections? Any advice would be appreciated, thank you

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  • Solving Big Problems with Oracle R Enterprise, Part II

    - by dbayard
    Part II – Solving Big Problems with Oracle R Enterprise In the first post in this series (see https://blogs.oracle.com/R/entry/solving_big_problems_with_oracle), we showed how you can use R to perform historical rate of return calculations against investment data sourced from a spreadsheet.  We demonstrated the calculations against sample data for a small set of accounts.  While this worked fine, in the real-world the problem is much bigger because the amount of data is much bigger.  So much bigger that our approach in the previous post won’t scale to meet the real-world needs. From our previous post, here are the challenges we need to conquer: The actual data that needs to be used lives in a database, not in a spreadsheet The actual data is much, much bigger- too big to fit into the normal R memory space and too big to want to move across the network The overall process needs to run fast- much faster than a single processor The actual data needs to be kept secured- another reason to not want to move it from the database and across the network And the process of calculating the IRR needs to be integrated together with other database ETL activities, so that IRR’s can be calculated as part of the data warehouse refresh processes In this post, we will show how we moved from sample data environment to working with full-scale data.  This post is based on actual work we did for a financial services customer during a recent proof-of-concept. Getting started with the Database At this point, we have some sample data and our IRR function.  We were at a similar point in our customer proof-of-concept exercise- we had sample data but we did not have the full customer data yet.  So our database was empty.  But, this was easily rectified by leveraging the transparency features of Oracle R Enterprise (see https://blogs.oracle.com/R/entry/analyzing_big_data_using_the).  The following code shows how we took our sample data SimpleMWRRData and easily turned it into a new Oracle database table called IRR_DATA via ore.create().  The code also shows how we can access the database table IRR_DATA as if it was a normal R data.frame named IRR_DATA. If we go to sql*plus, we can also check out our new IRR_DATA table: At this point, we now have our sample data loaded in the database as a normal Oracle table called IRR_DATA.  So, we now proceeded to test our R function working with database data. As our first test, we retrieved the data from a single account from the IRR_DATA table, pull it into local R memory, then call our IRR function.  This worked.  No SQL coding required! Going from Crawling to Walking Now that we have shown using our R code with database-resident data for a single account, we wanted to experiment with doing this for multiple accounts.  In other words, we wanted to implement the split-apply-combine technique we discussed in our first post in this series.  Fortunately, Oracle R Enterprise provides a very scalable way to do this with a function called ore.groupApply().  You can read more about ore.groupApply() here: https://blogs.oracle.com/R/entry/analyzing_big_data_using_the1 Here is an example of how we ask ORE to take our IRR_DATA table in the database, split it by the ACCOUNT column, apply a function that calls our SimpleMWRR() calculation, and then combine the results. (If you are following along at home, be sure to have installed our myIRR package on your database server via  “R CMD INSTALL myIRR”). The interesting thing about ore.groupApply is that the calculation is not actually performed in my desktop R environment from which I am running.  What actually happens is that ore.groupApply uses the Oracle database to perform the work.  And the Oracle database is what actually splits the IRR_DATA table by ACCOUNT.  Then the Oracle database takes the data for each account and sends it to an embedded R engine running on the database server to apply our R function.  Then the Oracle database combines all the individual results from the calls to the R function. This is significant because now the embedded R engine only needs to deal with the data for a single account at a time.  Regardless of whether we have 20 accounts or 1 million accounts or more, the R engine that performs the calculation does not care.  Given that normal R has a finite amount of memory to hold data, the ore.groupApply approach overcomes the R memory scalability problem since we only need to fit the data from a single account in R memory (not all of the data for all of the accounts). Additionally, the IRR_DATA does not need to be sent from the database to my desktop R program.  Even though I am invoking ore.groupApply from my desktop R program, because the actual SimpleMWRR calculation is run by the embedded R engine on the database server, the IRR_DATA does not need to leave the database server- this is both a performance benefit because network transmission of large amounts of data take time and a security benefit because it is harder to protect private data once you start shipping around your intranet. Another benefit, which we will discuss in a few paragraphs, is the ability to leverage Oracle database parallelism to run these calculations for dozens of accounts at once. From Walking to Running ore.groupApply is rather nice, but it still has the drawback that I run this from a desktop R instance.  This is not ideal for integrating into typical operational processes like nightly data warehouse refreshes or monthly statement generation.  But, this is not an issue for ORE.  Oracle R Enterprise lets us run this from the database using regular SQL, which is easily integrated into standard operations.  That is extremely exciting and the way we actually did these calculations in the customer proof. As part of Oracle R Enterprise, it provides a SQL equivalent to ore.groupApply which it refers to as “rqGroupEval”.  To use rqGroupEval via SQL, there is a bit of simple setup needed.  Basically, the Oracle Database needs to know the structure of the input table and the grouping column, which we are able to define using the database’s pipeline table function mechanisms. Here is the setup script: At this point, our initial setup of rqGroupEval is done for the IRR_DATA table.  The next step is to define our R function to the database.  We do that via a call to ORE’s rqScriptCreate. Now we can test it.  The SQL you use to run rqGroupEval uses the Oracle database pipeline table function syntax.  The first argument to irr_dataGroupEval is a cursor defining our input.  You can add additional where clauses and subqueries to this cursor as appropriate.  The second argument is any additional inputs to the R function.  The third argument is the text of a dummy select statement.  The dummy select statement is used by the database to identify the columns and datatypes to expect the R function to return.  The fourth argument is the column of the input table to split/group by.  The final argument is the name of the R function as you defined it when you called rqScriptCreate(). The Real-World Results In our real customer proof-of-concept, we had more sophisticated calculation requirements than shown in this simplified blog example.  For instance, we had to perform the rate of return calculations for 5 separate time periods, so the R code was enhanced to do so.  In addition, some accounts needed a time-weighted rate of return to be calculated, so we extended our approach and added an R function to do that.  And finally, there were also a few more real-world data irregularities that we needed to account for, so we added logic to our R functions to deal with those exceptions.  For the full-scale customer test, we loaded the customer data onto a Half-Rack Exadata X2-2 Database Machine.  As our half-rack had 48 physical cores (and 96 threads if you consider hyperthreading), we wanted to take advantage of that CPU horsepower to speed up our calculations.  To do so with ORE, it is as simple as leveraging the Oracle Database Parallel Query features.  Let’s look at the SQL used in the customer proof: Notice that we use a parallel hint on the cursor that is the input to our rqGroupEval function.  That is all we need to do to enable Oracle to use parallel R engines. Here are a few screenshots of what this SQL looked like in the Real-Time SQL Monitor when we ran this during the proof of concept (hint: you might need to right-click on these images to be able to view the images full-screen to see the entire image): From the above, you can notice a few things (numbers 1 thru 5 below correspond with highlighted numbers on the images above.  You may need to right click on the above images and view the images full-screen to see the entire image): The SQL completed in 110 seconds (1.8minutes) We calculated rate of returns for 5 time periods for each of 911k accounts (the number of actual rows returned by the IRRSTAGEGROUPEVAL operation) We accessed 103m rows of detailed cash flow/market value data (the number of actual rows returned by the IRR_STAGE2 operation) We ran with 72 degrees of parallelism spread across 4 database servers Most of our 110seconds was spent in the “External Procedure call” event On average, we performed 8,200 executions of our R function per second (110s/911k accounts) On average, each execution was passed 110 rows of data (103m detail rows/911k accounts) On average, we did 41,000 single time period rate of return calculations per second (each of the 8,200 executions of our R function did rate of return calculations for 5 time periods) On average, we processed over 900,000 rows of database data in R per second (103m detail rows/110s) R + Oracle R Enterprise: Best of R + Best of Oracle Database This blog post series started by describing a real customer problem: how to perform a lot of calculations on a lot of data in a short period of time.  While standard R proved to be a very good fit for writing the necessary calculations, the challenge of working with a lot of data in a short period of time remained. This blog post series showed how Oracle R Enterprise enables R to be used in conjunction with the Oracle Database to overcome the data volume and performance issues (as well as simplifying the operations and security issues).  It also showed that we could calculate 5 time periods of rate of returns for almost a million individual accounts in less than 2 minutes. In a future post, we will take the same R function and show how Oracle R Connector for Hadoop can be used in the Hadoop world.  In that next post, instead of having our data in an Oracle database, our data will live in Hadoop and we will how to use the Oracle R Connector for Hadoop and other Oracle Big Data Connectors to move data between Hadoop, R, and the Oracle Database easily.

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  • Big Data Matters with ODI12c

    - by Madhu Nair
    contributed by Mike Eisterer On October 17th, 2013, Oracle announced the release of Oracle Data Integrator 12c (ODI12c).  This release signifies improvements to Oracle’s Data Integration portfolio of solutions, particularly Big Data integration. Why Big Data = Big Business Organizations are gaining greater insights and actionability through increased storage, processing and analytical benefits offered by Big Data solutions.  New technologies and frameworks like HDFS, NoSQL, Hive and MapReduce support these benefits now. As further data is collected, analytical requirements increase and the complexity of managing transformations and aggregations of data compounds and organizations are in need for scalable Data Integration solutions. ODI12c provides enterprise solutions for the movement, translation and transformation of information and data heterogeneously and in Big Data Environments through: The ability for existing ODI and SQL developers to leverage new Big Data technologies. A metadata focused approach for cataloging, defining and reusing Big Data technologies, mappings and process executions. Integration between many heterogeneous environments and technologies such as HDFS and Hive. Generation of Hive Query Language. Working with Big Data using Knowledge Modules  ODI12c provides developers with the ability to define sources and targets and visually develop mappings to effect the movement and transformation of data.  As the mappings are created, ODI12c leverages a rich library of prebuilt integrations, known as Knowledge Modules (KMs).  These KMs are contextual to the technologies and platforms to be integrated.  Steps and actions needed to manage the data integration are pre-built and configured within the KMs.  The Oracle Data Integrator Application Adapter for Hadoop provides a series of KMs, specifically designed to integrate with Big Data Technologies.  The Big Data KMs include: Check Knowledge Module Reverse Engineer Knowledge Module Hive Transform Knowledge Module Hive Control Append Knowledge Module File to Hive (LOAD DATA) Knowledge Module File-Hive to Oracle (OLH-OSCH) Knowledge Module  Nothing to beat an Example: To demonstrate the use of the KMs which are part of the ODI Application Adapter for Hadoop, a mapping may be defined to move data between files and Hive targets.  The mapping is defined by dragging the source and target into the mapping, performing the attribute (column) mapping (see Figure 1) and then selecting the KM which will govern the process.  In this mapping example, movie data is being moved from an HDFS source into a Hive table.  Some of the attributes, such as “CUSTID to custid”, have been mapped over. Figure 1  Defining the Mapping Before the proper KM can be assigned to define the technology for the mapping, it needs to be added to the ODI project.  The Big Data KMs have been made available to the project through the KM import process.   Generally, this is done prior to defining the mapping. Figure 2  Importing the Big Data Knowledge Modules Following the import, the KMs are available in the Designer Navigator. v\:* {behavior:url(#default#VML);} o\:* {behavior:url(#default#VML);} w\:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);} Normal 0 false false false EN-US ZH-TW X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";} Figure 3  The Project View in Designer, Showing Installed IKMs Once the KM is imported, it may be assigned to the mapping target.  This is done by selecting the Physical View of the mapping and examining the Properties of the Target.  In this case MOVIAPP_LOG_STAGE is the target of our mapping. Figure 4  Physical View of the Mapping and Assigning the Big Data Knowledge Module to the Target Alternative KMs may have been selected as well, providing flexibility and abstracting the logical mapping from the physical implementation.  Our mapping may be applied to other technologies as well. The mapping is now complete and is ready to run.  We will see more in a future blog about running a mapping to load Hive. To complete the quick ODI for Big Data Overview, let us take a closer look at what the IKM File to Hive is doing for us.  ODI provides differentiated capabilities by defining the process and steps which normally would have to be manually developed, tested and implemented into the KM.  As shown in figure 5, the KM is preparing the Hive session, managing the Hive tables, performing the initial load from HDFS and then performing the insert into Hive.  HDFS and Hive options are selected graphically, as shown in the properties in Figure 4. Figure 5  Process and Steps Managed by the KM What’s Next Big Data being the shape shifting business challenge it is is fast evolving into the deciding factor between market leaders and others. Now that an introduction to ODI and Big Data has been provided, look for additional blogs coming soon using the Knowledge Modules which make up the Oracle Data Integrator Application Adapter for Hadoop: Importing Big Data Metadata into ODI, Testing Data Stores and Loading Hive Targets Generating Transformations using Hive Query language Loading Oracle from Hadoop Sources For more information now, please visit the Oracle Data Integrator Application Adapter for Hadoop web site, http://www.oracle.com/us/products/middleware/data-integration/hadoop/overview/index.html Do not forget to tune in to the ODI12c Executive Launch webcast on the 12th to hear more about ODI12c and GG12c. Normal 0 false false false EN-US ZH-TW X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";}

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  • Data Security Through Structure, Procedures, Policies, and Governance

    Security Structure and Procedures One of the easiest ways to implement security is through the use of structure, in particular the structure in which data is stored. The preferred method for this through the use of User Roles, these Roles allow for specific access to be granted based on what role a user plays in relation to the data that they are manipulating. Typical data access actions are defined by the CRUD Principle. CRUD Principle: Create New Data Read Existing Data Update Existing Data Delete Existing Data Based on the actions assigned to a role assigned, User can manipulate data as they need to preform daily business operations.  An example of this can be seen in a hospital where doctors have been assigned Create, Read, Update, and Delete access to their patient’s prescriptions so that a doctor can prescribe and adjust any existing prescriptions as necessary. However, a nurse will only have Read access on the patient’s prescriptions so that they will know what medicines to give to the patients. If you notice, they do not have access to prescribe new prescriptions, update or delete existing prescriptions because only the patient’s doctor has access to preform those actions. With User Roles comes responsibility, companies need to constantly monitor data access to ensure that the proper roles have the most appropriate access levels to ensure users are not exposed to inappropriate data.  In addition this also protects rouge employees from gaining access to critical business information that could be destroyed, altered or stolen. It is important that all data access is monitored because of this threat. Security Governance Current Data Governance laws regarding security Health Insurance Portability and Accountability Act (HIPAA) Sarbanes-Oxley Act Database Breach Notification Act The US Department of Health and Human Services defines HIIPAA as a Privacy Rule. This legislation protects the privacy of individually identifiable health information. Currently, HIPAA   sets the national standards for securing electronically protected health records. Additionally, its confidentiality provisions protect identifiable information being used to analyze patient safety events and improve patient safety. In 2002 after the wake of the Enron and World Com Financial scandals Senator Paul Sarbanes and Representative Michael Oxley lead the creation of the Sarbanes-Oxley Act. This act administered by the Securities and Exchange Commission (SEC) dramatically altered corporate financial practices and data governance. In addition, it also set specific deadlines for compliance. The Sarbanes-Oxley is not a set of standard business rules and does not specify how a company should retain its records; In fact, this act outlines which pieces of data are to be stored as well as the storage duration. The Database Breach Notification Act requires companies, in the event of a data breach containing personally identifiable information, to notify all California residents whose information was stored on the compromised system at the time of the event, according to Gregory Manter. He further explains that this act is only California legislation. However, it does affect “any person or business that conducts business in California, and that owns or licenses computerized data that includes personal information,” regardless of where the compromised data is located.  This will force any business that maintains at least limited interactions with California residents will find themselves subject to the Act’s provisions. Security Policies All companies must work in accordance with the appropriate city, county, state, and federal laws. One way to ensure that a company is legally compliant is to enforce security policies that adhere to the appropriate legislation in their area or areas that they service. These types of polices need to be mandated by a company’s Security Officer. For smaller companies, these policies need to come from executives, Directors, and Owners.

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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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  • Programmaticaly finding the Landau notation (Big O or Theta notation) of an algorithm?

    - by Julien L
    I'm used to search for the Landau (Big O, Theta...) notation of my algorithms by hand to make sure they are as optimized as they can be, but when the functions are getting really big and complex, it's taking way too much time to do it by hand. it's also prone to human errors. I spent some time on Codility (coding/algo exercises), and noticed they will give you the Landau notation for your submitted solution (both in Time and Memory usage). I was wondering how they do that... How would you do it? Is there another way besides Lexical Analysis or parsing of the code? PS: This question concerns mainly PHP and or JavaScript, but I'm opened to any language and theory.

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  • Windows Azure Recipe: Big Data

    - by Clint Edmonson
    As the name implies, what we’re talking about here is the explosion of electronic data that comes from huge volumes of transactions, devices, and sensors being captured by businesses today. This data often comes in unstructured formats and/or too fast for us to effectively process in real time. Collectively, we call these the 4 big data V’s: Volume, Velocity, Variety, and Variability. These qualities make this type of data best managed by NoSQL systems like Hadoop, rather than by conventional Relational Database Management System (RDBMS). We know that there are patterns hidden inside this data that might provide competitive insight into market trends.  The key is knowing when and how to leverage these “No SQL” tools combined with traditional business such as SQL-based relational databases and warehouses and other business intelligence tools. Drivers Petabyte scale data collection and storage Business intelligence and insight Solution The sketch below shows one of many big data solutions using Hadoop’s unique highly scalable storage and parallel processing capabilities combined with Microsoft Office’s Business Intelligence Components to access the data in the cluster. Ingredients Hadoop – this big data industry heavyweight provides both large scale data storage infrastructure and a highly parallelized map-reduce processing engine to crunch through the data efficiently. Here are the key pieces of the environment: Pig - a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. Mahout - a machine learning library with algorithms for clustering, classification and batch based collaborative filtering that are implemented on top of Apache Hadoop using the map/reduce paradigm. Hive - data warehouse software built on top of Apache Hadoop that facilitates querying and managing large datasets residing in distributed storage. Directly accessible to Microsoft Office and other consumers via add-ins and the Hive ODBC data driver. Pegasus - a Peta-scale graph mining system that runs in parallel, distributed manner on top of Hadoop and that provides algorithms for important graph mining tasks such as Degree, PageRank, Random Walk with Restart (RWR), Radius, and Connected Components. Sqoop - a tool designed for efficiently transferring bulk data between Apache Hadoop and structured data stores such as relational databases. Flume - a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large log data amounts to HDFS. Database – directly accessible to Hadoop via the Sqoop based Microsoft SQL Server Connector for Apache Hadoop, data can be efficiently transferred to traditional relational data stores for replication, reporting, or other needs. Reporting – provides easily consumable reporting when combined with a database being fed from the Hadoop environment. Training These links point to online Windows Azure training labs where you can learn more about the individual ingredients described above. Hadoop Learning Resources (20+ tutorials and labs) Huge collection of resources for learning about all aspects of Apache Hadoop-based development on Windows Azure and the Hadoop and Windows Azure Ecosystems SQL Azure (7 labs) Microsoft SQL Azure delivers on the Microsoft Data Platform vision of extending the SQL Server capabilities to the cloud as web-based services, enabling you to store structured, semi-structured, and unstructured data. See my Windows Azure Resource Guide for more guidance on how to get started, including links web portals, training kits, samples, and blogs related to Windows Azure.

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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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  • Big-O complexity of c^n + n*(logn)^2 + (10*n)^c

    - by zebraman
    I need to derive the Big-O complexity of this expression: c^n + n*(log(n))^2 + (10*n)^c where c is a constant and n is a variable. I'm pretty sure I understand how to derive the Big-O complexity of each term individually, I just don't know how the Big-O complexity changes when the terms are combined like this. Ideas? Any help would be great, thanks.

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  • TDWI World Conference Features Oracle and Big Data

    - by Mandy Ho
    Oracle is a Gold Sponsor at this year's TDWI World Conference Series, held at the Manchester Grand Hyatt in San Diego, California - July 31 to Aug 1. The theme of this event is Big Data Tipping Point: BI Strategies in the Era of Big Data. The conference features an educational look at how data is now being generated so quickly that organizations across all industries need new technologies to stay ahead - to understand customer behavior, detect fraud, improve processes and accelerate performance. Attendees will hear how the internet, social media and streaming data are fundamentally changing business intelligence and data warehousing. Big data is reaching critical mass - the tipping point. Oracle will be conducting the following Evening Workshop. To reserve your space, call 1.800.820.5592 ext 10775. Title...:    Integrating Big Data into Your Data Center (or A Big Data Reference Architecture) Date.:    Wed., August 1, 2012, at 7:00 p.m Venue:: Manchester Grand Hyatt, San Diego, Room Weblogs, Social Media, smart meters, senors and other devices generate high volumes of low density information that isn't readily accessible in enterprise data warehouses and business intelligence applications today. But, this data can have relevant business value, especially when analyzed alongside traditional information sources. In this session, we will outline a reference architecture for big data that will help you maximize the value of your big data implementation. You will learn: The key technologies in a big architecture, and their specific use case The integration point of the various technologies and how they fit into your existing IT environment How effectively leverage analytical sandboxes for data discovery and agile development of data driven solutions   At the end of this session you will understand the reference architecture and have the tools to implement this architecture at your company. Presenter: Jean-Pierre Dijcks, Senior Principal Product Manager Don't miss our booth and the chance to meet with our Big data experts on the exhibition floor at booth #306. 

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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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  • BAM Data Control in multiple ADF Faces Components

    - by [email protected]
    As we know Oracle BAM data control instance sharing is not supported.When two or more ADF Faces components must display the same data, and are bound to the same Oracle BAM data control definition, we have to make sure that we wrap each ADF Faces component in an ADF task flow, and set the Data Control Scope to isolated. This blog will show a small sample to demonstrate this. In this sample we will create a Pie and Bar using same BAM DC, such that both components use same Data control but have isolated scope.This sample can be downloaded  fromSample1.zip Set-up: Create a BAM data control using employees DO (sample) Steps: Right click on View Controller project and select "New->ADF Task Flow" Check "Create Bounded Task Flow" and give some meaningful name (ex:EmpPieTF.xml ) to the TaskFlow(TF) and click on "OK"CreateTF.bmpFrom the "Components Palette", drag and drop "View" into the task flow diagram. Give a meaningful name to the view. Double Click and Click "Ok" for  "Create New JSF Page Fragment" From "Data Controls" drag and drop "Employees->Query"  into this jsff page as "Graph->Pie" (Pie: Sales_Number and Slices: Salesperson) Repeat step 1 through 4 for another Task Flow (ex: EmpBarTF). From "Data Controls" drag and drop "Employees->Query"  into this jsff page as "Graph->Bar" (Bars :Sales_Number and X-axis : Salesperson). Open the Taskflow created in step 2. In the Structure Pane, right click on "Task Flow Definition -EmpPieTF" Click "Insert inside Task Flow Definition - EmpPieTF -> ADF Task Flow -> Data Control Scope". Click "OK"TFDCScope.bmpFor the "Data Control Scope", In the Property Inspector ->General section, change data control scope from Shared to Isolated. Repeat step 8 through 11 for the 2nd Task flow created. Now create a new jspx page example: Main.jspxDrag and drop both the Task flows (ex: "EmpPieTF" and "EmpBarTF") as regions. Surround with panel components as needed.Run the page Main.jspxMainPage.bmpNow when the page runs although both components are created using same Data control the bindings are not shared and each component will have a separate instance of the data control.

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  • Big Data: Size isn’t everything

    - by Simon Elliston Ball
    Big Data has a big problem; it’s the word “Big”. These days, a quick Google search will uncover terabytes of negative opinion about the futility of relying on huge volumes of data to produce magical, meaningful insight. There are also many clichéd but correct assertions about the difficulties of correlation versus causation, in massive data sets. In reading some of these pieces, I begin to understand how climatologists must feel when people complain ironically about “global warming” during snowfall. Big Data has a name problem. There is a lot more to it than size. Shape, Speed, and…err…Veracity are also key elements (now I understand why Gartner and the gang went with V’s instead of S’s). The need to handle data of different shapes (Variety) is not new. Data developers have always had to mold strange-shaped data into our reporting systems, integrating with semi-structured sources, and even straying into full-text searching. However, what we lacked was an easy way to add semi-structured and unstructured data to our arsenal. New “Big Data” tools such as MongoDB, and other NoSQL (Not Only SQL) databases, or a graph database like Neo4J, fill this gap. Still, to many, they simply introduce noise to the clean signal that is their sensibly normalized data structures. What about speed (Velocity)? It’s not just high frequency trading that generates data faster than a single system can handle. Many other applications need to make trade-offs that traditional databases won’t, in order to cope with high data insert speeds, or to extract quickly the required information from data streams. Unfortunately, many people equate Big Data with the Hadoop platform, whose batch driven queries and job processing queues have little to do with “velocity”. StreamInsight, Esper and Tibco BusinessEvents are examples of Big Data tools designed to handle high-velocity data streams. Again, the name doesn’t do the discipline of Big Data any favors. Ultimately, though, does analyzing fast moving data produce insights as useful as the ones we get through a more considered approach, enabled by traditional BI? Finally, we have Veracity and Value. In many ways, these additions to the classic Volume, Velocity and Variety trio acknowledge the criticism that without high-quality data and genuinely valuable outputs then data, big or otherwise, is worthless. As a discipline, Big Data has recognized this, and data quality and cleaning tools are starting to appear to support it. Rather than simply decrying the irrelevance of Volume, we need as a profession to focus how to improve Veracity and Value. Perhaps we should just declare the ‘Big’ silent, embrace these new data tools and help develop better practices for their use, just as we did the good old RDBMS? What does Big Data mean to you? Which V gives your business the most pain, or the most value? Do you see these new tools as a useful addition to the BI toolbox, or are they just enabling a dangerous trend to find ghosts in the noise?

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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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  • When is BIG, big enough for a database?

    - by David ???
    I'm developing a Java application that has performance at its core. I have a list of some 40,000 "final" objects, i.e., I have an initialization input data of 40,000 vectors. This data is unchanged throughout the program's run. I am always preforming lookups against a single ID property to retrieve the proper vectors. Currently I am using a HashMap over a sub-sample of a 1,000 vectors, but I'm not sure it will scale to production. When is BIG, actually big enough for a use of DB? One more thing, an SQLite DB is a viable option as no concurrency is involved, so I guess the "threshold" for db use, is perhaps lower.

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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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