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  • Globacom and mCentric Deploy BDA and NoSQL Database to analyze network traffic 40x faster

    - by Jean-Pierre Dijcks
    In a fast evolving market, speed is of the essence. mCentric and Globacom leveraged Big Data Appliance, Oracle NoSQL Database to save over 35,000 Call-Processing minutes daily and analyze network traffic 40x faster.  Here are some highlights from the profile: Why Oracle “Oracle Big Data Appliance works well for very large amounts of structured and unstructured data. It is the most agile events-storage system for our collect-it-now and analyze-it-later set of business requirements. Moreover, choosing a prebuilt solution drastically reduced implementation time. We got the big data benefits without needing to assemble and tune a custom-built system, and without the hidden costs required to maintain a large number of servers in our data center. A single support license covers both the hardware and the integrated software, and we have one central point of contact for support,” said Sanjib Roy, CTO, Globacom. Implementation Process It took only five days for Oracle partner mCentric to deploy Oracle Big Data Appliance, perform the software install and configuration, certification, and resiliency testing. The entire process—from site planning to phase-I, go-live—was executed in just over ten weeks, well ahead of the four months allocated to complete the project. Oracle partner mCentric leveraged Oracle Advanced Customer Support Services’ implementation methodology to ensure configurations are tailored for peak performance, all patches are applied, and software and communications are consistently tested using proven methodologies and best practices. Read the entire profile here.

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  • SQL – Migrate Database from SQL Server to NuoDB – A Quick Tutorial

    - by Pinal Dave
    Data is growing exponentially and every organization with growing data is thinking of next big innovation in the world of Big Data. Big data is a indeed a future for every organization at one point of the time. Just like every other next big thing, big data has its own challenges and issues. The biggest challenge associated with the big data is to find the ideal platform which supports the scalability and growth of the data. If you are a regular reader of this blog, you must be familiar with NuoDB. I have been working with NuoDB for a while and their recent release is the best thus far. NuoDB is an elastically scalable SQL database that can run on local host, datacenter and cloud-based resources. A key feature of the product is that it does not require sharding (read more here). Last week, I was able to install NuoDB in less than 90 seconds and have explored their Explorer and Admin sections. You can read about my experiences in these posts: SQL – Step by Step Guide to Download and Install NuoDB – Getting Started with NuoDB SQL – Quick Start with Admin Sections of NuoDB – Manage NuoDB Database SQL – Quick Start with Explorer Sections of NuoDB – Query NuoDB Database Many SQL Authority readers have been following me in my journey to evaluate NuoDB. One of the frequently asked questions I’ve received from you is if there is any way to migrate data from SQL Server to NuoDB. The fact is that there is indeed a way to do so and NuoDB provides a fantastic tool which can help users to do it. NuoDB Migrator is a command line utility that supports the migration of Microsoft SQL Server, MySQL, Oracle, and PostgreSQL schemas and data to NuoDB. The migration to NuoDB is a three-step process: NuoDB Migrator generates a schema for a target NuoDB database It loads data into the target NuoDB database It dumps data from the source database Let’s see how we can migrate our data from SQL Server to NuoDB using a simple three-step approach. But before we do that we will create a sample database in MSSQL and later we will migrate the same database to NuoDB: Setup Step 1: Build a sample data CREATE DATABASE [Test]; CREATE TABLE [Department]( [DepartmentID] [smallint] NOT NULL, [Name] VARCHAR(100) NOT NULL, [GroupName] VARCHAR(100) NOT NULL, [ModifiedDate] [datetime] NOT NULL, CONSTRAINT [PK_Department_DepartmentID] PRIMARY KEY CLUSTERED ( [DepartmentID] ASC ) ) ON [PRIMARY]; INSERT INTO Department SELECT * FROM AdventureWorks2012.HumanResources.Department; Note that I am using the SQL Server AdventureWorks database to build this sample table but you can build this sample table any way you prefer. Setup Step 2: Install Java 64 bit Before you can begin the migration process to NuoDB, make sure you have 64-bit Java installed on your computer. This is due to the fact that the NuoDB Migrator tool is built in Java. You can download 64-bit Java for Windows, Mac OSX, or Linux from the following link: http://java.com/en/download/manual.jsp. One more thing to remember is that you make sure that the path in your environment settings is set to your JAVA_HOME directory or else the tool will not work. Here is how you can do it: Go to My Computer >> Right Click >> Select Properties >> Click on Advanced System Settings >> Click on Environment Variables >> Click on New and enter the following values. Variable Name: JAVA_HOME Variable Value: C:\Program Files\Java\jre7 Make sure you enter your Java installation directory in the Variable Value field. Setup Step 3: Install JDBC driver for SQL Server. There are two JDBC drivers available for SQL Server.  Select the one you prefer to use by following one of the two links below: Microsoft JDBC Driver jTDS JDBC Driver In this example we will be using jTDS JDBC driver. Once you download the driver, move the driver to your NuoDB installation folder. In my case, I have moved the JAR file of the driver into the C:\Program Files\NuoDB\tools\migrator\jar folder as this is my NuoDB installation directory. Now we are all set to start the three-step migration process from SQL Server to NuoDB: Migration Step 1: NuoDB Schema Generation Here is the command I use to generate a schema of my SQL Server Database in NuoDB. First I go to the folder C:\Program Files\NuoDB\tools\migrator\bin and execute the nuodb-migrator.bat file. Note that my database name is ‘test’. Additionally my username and password is also ‘test’. You can see that my SQL Server database is running on my localhost on port 1433. Additionally, the schema of the table is ‘dbo’. nuodb-migrator schema –source.driver=net.sourceforge.jtds.jdbc.Driver –source.url=jdbc:jtds:sqlserver://localhost:1433/ –source.username=test –source.password=test –source.catalog=test –source.schema=dbo –output.path=/tmp/schema.sql The above script will generate a schema of all my SQL Server tables and will put it in the folder C:\tmp\schema.sql . You can open the schema.sql file and execute this file directly in your NuoDB instance. You can follow the link here to see how you can execute the SQL script in NuoDB. Please note that if you have not yet created the schema in the NuoDB database, you should create it before executing this step. Step 2: Generate the Dump File of the Data Once you have recreated your schema in NuoDB from SQL Server, the next step is very easy. Here we create a CSV format dump file, which will contain all the data from all the tables from the SQL Server database. The command to do so is very similar to the above command. Be aware that this step may take a bit of time based on your database size. nuodb-migrator dump –source.driver=net.sourceforge.jtds.jdbc.Driver –source.url=jdbc:jtds:sqlserver://localhost:1433/ –source.username=test –source.password=test –source.catalog=test –source.schema=dbo –output.type=csv –output.path=/tmp/dump.cat Once the above command is successfully executed you can find your CSV file in the C:\tmp\ folder. However, you do not have to do anything manually. The third and final step will take care of completing the migration process. Migration Step 3: Load the Data into NuoDB After building schema and taking a dump of the data, the very next step is essential and crucial. It will take the CSV file and load it into the NuoDB database. nuodb-migrator load –target.url=jdbc:com.nuodb://localhost:48004/mytest –target.schema=dbo –target.username=test –target.password=test –input.path=/tmp/dump.cat Please note that in the above script we are now targeting the NuoDB database, which we have already created with the name of “MyTest”. If the database does not exist, create it manually before executing the above script. I have kept the username and password as “test”, but please make sure that you create a more secure password for your database for security reasons. Voila!  You’re Done That’s it. You are done. It took 3 setup and 3 migration steps to migrate your SQL Server database to NuoDB.  You can now start exploring the database and build excellent, scale-out applications. In this blog post, I have done my best to come up with simple and easy process, which you can follow to migrate your app from SQL Server to NuoDB. Download NuoDB I strongly encourage you to download NuoDB and go through my 3-step migration tutorial from SQL Server to NuoDB. Additionally here are two very important blog post from NuoDB CTO Seth Proctor. He has written excellent blog posts on the concept of the Administrative Domains. NuoDB has this concept of an Administrative Domain, which is a collection of hosts that can run one or multiple databases.  Each database has its own TEs and SMs, but all are managed within the Admin Console for that particular domain. http://www.nuodb.com/techblog/2013/03/11/getting-started-provisioning-a-domain/ http://www.nuodb.com/techblog/2013/03/14/getting-started-running-a-database/ 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, Technology Tagged: NuoDB

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  • MapRedux - PowerShell and Big Data

    - by Dittenhafer Solutions
    MapRedux – #PowerShell and #Big Data Have you been hearing about “big data”, “map reduce” and other large scale computing terms over the past couple of years and been curious to dig into more detail? Have you read some of the Apache Hadoop online documentation and unfortunately concluded that it wasn't feasible to setup a “test” hadoop environment on your machine? More recently, I have read about some of Microsoft’s work to enable Hadoop on the Azure cloud. Being a "Microsoft"-leaning technologist, I am more inclinded to be successful with experimentation when on the Windows platform. Of course, it is not that I am "religious" about one set of technologies other another, but rather more experienced. Anyway, within the past couple of weeks I have been thinking about PowerShell a bit more as the 2012 PowerShell Scripting Games approach and it occured to me that PowerShell's support for Windows Remote Management (WinRM), and some other inherent features of PowerShell might lend themselves particularly well to a simple implementation of the MapReduce framework. I fired up my PowerShell ISE and started writing just to see where it would take me. Quite simply, the ScriptBlock feature combined with the ability of Invoke-Command to create remote jobs on networked servers provides much of the plumbing of a distributed computing environment. There are some limiting factors of course. Microsoft provided some default settings which prevent PowerShell from taking over a network without administrative approval first. But even with just one adjustment, a given Windows-based machine can become a node in a MapReduce-style distributed computing environment. Ok, so enough introduction. Let's talk about the code. First, any machine that will participate as a remote "node" will need WinRM enabled for remote access, as shown below. This is not exactly practical for hundreds of intended nodes, but for one (or five) machines in a test environment it does just fine. C:> winrm quickconfig WinRM is not set up to receive requests on this machine. The following changes must be made: Set the WinRM service type to auto start. Start the WinRM service. Make these changes [y/n]? y Alternatively, you could take the approach described in the Remotely enable PSRemoting post from the TechNet forum and use PowerShell to create remote scheduled tasks that will call Enable-PSRemoting on each intended node. Invoke-MapRedux Moving on, now that you have one or more remote "nodes" enabled, you can consider the actual Map and Reduce algorithms. Consider the following snippet: $MyMrResults = Invoke-MapRedux -MapReduceItem $Mr -ComputerName $MyNodes -DataSet $dataset -Verbose Invoke-MapRedux takes an instance of a MapReduceItem which references the Map and Reduce scriptblocks, an array of computer names which are the remote nodes, and the initial data set to be processed. As simple as that, you can start working with concepts of big data and the MapReduce paradigm. Now, how did we get there? I have published the initial version of my PsMapRedux PowerShell Module on GitHub. The PsMapRedux module provides the Invoke-MapRedux function described above. Feel free to browse the underlying code and even contribute to the project! In a later post, I plan to show some of the inner workings of the module, but for now let's move on to how the Map and Reduce functions are defined. Map Both the Map and Reduce functions need to follow a prescribed prototype. The prototype for a Map function in the MapRedux module is as follows. A simple scriptblock that takes one PsObject parameter and returns a hashtable. It is important to note that the PsObject $dataset parameter is a MapRedux custom object that has a "Data" property which offers an array of data to be processed by the Map function. $aMap = { Param ( [PsObject] $dataset ) # Indicate the job is running on the remote node. Write-Host ($env:computername + "::Map"); # The hashtable to return $list = @{}; # ... Perform the mapping work and prepare the $list hashtable result with your custom PSObject... # ... The $dataset has a single 'Data' property which contains an array of data rows # which is a subset of the originally submitted data set. # Return the hashtable (Key, PSObject) Write-Output $list; } Reduce Likewise, with the Reduce function a simple prototype must be followed which takes a $key and a result $dataset from the MapRedux's partitioning function (which joins the Map results by key). Again, the $dataset is a MapRedux custom object that has a "Data" property as described in the Map section. $aReduce = { Param ( [object] $key, [PSObject] $dataset ) Write-Host ($env:computername + "::Reduce - Count: " + $dataset.Data.Count) # The hashtable to return $redux = @{}; # Return Write-Output $redux; } All Together Now When everything is put together in a short example script, you implement your Map and Reduce functions, query for some starting data, build the MapReduxItem via New-MapReduxItem and call Invoke-MapRedux to get the process started: # Import the MapRedux and SQL Server providers Import-Module "MapRedux" Import-Module “sqlps” -DisableNameChecking # Query the database for a dataset Set-Location SQLSERVER:\sql\dbserver1\default\databases\myDb $query = "SELECT MyKey, Date, Value1 FROM BigData ORDER BY MyKey"; Write-Host "Query: $query" $dataset = Invoke-SqlCmd -query $query # Build the Map function $MyMap = { Param ( [PsObject] $dataset ) Write-Host ($env:computername + "::Map"); $list = @{}; foreach($row in $dataset.Data) { # Write-Host ("Key: " + $row.MyKey.ToString()); if($list.ContainsKey($row.MyKey) -eq $true) { $s = $list.Item($row.MyKey); $s.Sum += $row.Value1; $s.Count++; } else { $s = New-Object PSObject; $s | Add-Member -Type NoteProperty -Name MyKey -Value $row.MyKey; $s | Add-Member -type NoteProperty -Name Sum -Value $row.Value1; $list.Add($row.MyKey, $s); } } Write-Output $list; } $MyReduce = { Param ( [object] $key, [PSObject] $dataset ) Write-Host ($env:computername + "::Reduce - Count: " + $dataset.Data.Count) $redux = @{}; $count = 0; foreach($s in $dataset.Data) { $sum += $s.Sum; $count += 1; } # Reduce $redux.Add($s.MyKey, $sum / $count); # Return Write-Output $redux; } # Create the item data $Mr = New-MapReduxItem "My Test MapReduce Job" $MyMap $MyReduce # Array of processing nodes... $MyNodes = ("node1", "node2", "node3", "node4", "localhost") # Run the Map Reduce routine... $MyMrResults = Invoke-MapRedux -MapReduceItem $Mr -ComputerName $MyNodes -DataSet $dataset -Verbose # Show the results Set-Location C:\ $MyMrResults | Out-GridView Conclusion I hope you have seen through this article that PowerShell has a significant infrastructure available for distributed computing. While it does take some code to expose a MapReduce-style framework, much of the work is already done and PowerShell could prove to be the the easiest platform to develop and run big data jobs in your corporate data center, potentially in the Azure cloud, or certainly as an academic excerise at home or school. Follow me on Twitter to stay up to date on the continuing progress of my Powershell MapRedux module, and thanks for reading! Daniel

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  • Data Integration 12c Raising the Big Data Roof at Oracle OpenWorld

    - by Tanu Sood
    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-family:"Times New Roman","serif"; mso-fareast-font-family:"MS Mincho";} Author: Dain Hansen, Director, Oracle It was an exciting OpenWorld 2013 for us in the Data Integration track. Our theme this year was all about ‘being future ready’ - previewing one of our biggest releases this year: Oracle Data Integration 12c. Just this week we followed up with this preview by announcing the general availability of 12c release for Oracle’s key data integration products: Oracle Data Integrator 12c and Oracle GoldenGate 12c. The new release delivers extreme performance, increase IT productivity, and simplify deployment, while helping IT organizations to keep pace with new data-oriented technology trends including cloud computing, big data analytics, real-time business intelligence. 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-family:"Times New Roman","serif"; mso-fareast-font-family:"MS Mincho";} Mark Hurd's keynote on day one set the tone for the Data Integration sessions. Mark focused on big data analytics and the changing consumer expectations. Especially real-time insight is a key theme for Oracle overall and data integration products. In Mark Hurd's keynote we heard from key customers, such as Airbus and Thomson Reuters, how real-time analysis of operational data including machine data creates value, in some cases even saves lives. Thomas Kurian gave a deeper look into Oracle's big data and fast data solutions. In the initial lead Data Integration track session - Brad Adelberg, VP of Development, presented Oracle’s Data Integration 12c product strategy based on key trends from the initial OpenWorld keynotes. Brad talked about how Oracle's data integration products address the new data integration requirements that evolved with cloud computing, big data, and changing consumer expectations and how they set the key themes in our products’ road map. Brad explained why and how fast-time to value, high-performance and future-ready solutions is the top focus areas for product development. If you were not able to attend OpenWorld or this session I recommend reading the white paper: Five New Data Integration Requirements and How to Meet them with Oracle Data Integration, which provides an in-depth look into how Oracle addresses the new trends in the DI market. Following Brad’s session, Nick Wagner provided in depth review of Oracle GoldenGate’s latest features and roadmap. Nick discussed how Oracle GoldenGate’s tight integration with Oracle Database sets the product apart from the competition. We also heard that heterogeneity of the product is still a major focus for GoldenGate’s development and there will be more news on that front when there is a major release. 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-family:"Times New Roman","serif"; mso-fareast-font-family:"MS Mincho";} After GoldenGate’s product strategy session, Denis Gray from the PM team presented Oracle Data Integrator’s product strategy session, talking about the latest and greatest on ODI. Another good session was delivered by long-time GoldenGate users, Comcast.  Jason Hurd and Amit Patel of Comcast talked about the various use cases they deploy Oracle GoldenGate throughout their enterprise, from database upgrades, feeding reporting systems, to active-active database synchronization.  The Comcast team shared many good tips on how to use GoldenGate for both zero downtime upgrades and active-active replication with conflict management requirement. One of our other important goals we had this year for the Data Integration track at OpenWorld was hearing from our customers. We ended day 1 on just that, with a wonderful award ceremony for Oracle Excellence Awards for Oracle Fusion Middleware Innovation. The ceremony was held in the Yerba Buena Center for the Arts. Congratulations to Royal Bank of Scotland and Yalumba Wine Company, the winners in the Data Integration category. You can find more information on the award and the winners in our previous blog post: 2013 Oracle Excellence Awards for Fusion Middleware Innovation… Selected for their innovation use of Oracle’s Data Integration products; the winners for the Data Integration Category are Royal Bank of Scotland and The Yalumba Wine Company. Congratulations!!! Royal Bank of Scotland’s Market and International Banking division provides clients across the globe with seamless trading and competitive pricing, underpinned by a deep knowledge of risk management across the full spectrum of financial products. They handle millions of transactions daily to keep the lifeblood of their clients’ businesses flowing – whether through payment management solutions or through bespoke trade finance solutions. Royal Bank of Scotland is leveraging Oracle GoldenGate and Oracle Data Integrator along with Oracle Business Intelligence Enterprise Edition and the Oracle Database for a variety of solutions. Mainly, Oracle GoldenGate and Oracle Data Integrator are used to feed their data warehouse – providing a real-time data integration solution that feeds transactional data to their analytics system in minutes to enable improved decision making with timely, accurate data for their business users. Oracle Data Integrator’s in-database transformation capabilities and its ability to integrate with Oracle GoldenGate for real-time data capture is the foundation of this implementation. This solution makes it such that changes happening in the analytics systems are available the same day they are deployed on the operational system with 100% data quality guaranteed. Additionally, the solution has helped to reduce their operational database size from 150GB to 10GB. Impressive! Now what if I told you this solution was built in 3 months and had a less than 6 month return on investment? That’s outstanding! The Yalumba Wine Company is situated in the Barossa Valley of Australia. It is the oldest family owned winery in Australia with a unique way of aging their wines in specially crafted 100 liter barrels. Did you know that “Yalumba” is Aboriginal for “all the land around”? The Yalumba Wine Company is growing rapidly, and was in need of introducing a more modern standard to the existing manufacturing processes to meet globalization demands, overall time-to-market, and better operational efficiency objectives of product development. The Yalumba Wine Company worked with a partner, Bristlecone to develop a unique solution whereby Oracle Data Integrator is leveraged to pull data from Salesforce.com and JD Edwards, in addition to their other pre-existing source systems, for consumption into their data warehouse. They have emphasized the overall ease of developing integration workflows with Oracle Data Integrator. The solution has brought better visibility for the business users, shorter data loading and transformation performance to their data warehouse with rapid incorporation of new data sources, and a solid future-proof foundation for their organization. Moving forward, they plan on leveraging more from Oracle’s Data Integration portfolio. Terrific! In addition to these two customers on Tuesday we featured many other important Oracle Data Integrator and Oracle GoldenGate customers. On Tuesday the GoldenGate panel included: Land O’Lakes, Smuckers, and Veolia Water. Besides giving us yummy nutrition and healthy water, these companies have another aspect in common. They all use GoldenGate to boost their ERP application. Please read the recap by Irem Radzik. On Wednesday, the ODI Panel included: Barry Ralston and Ryan Weber of Infinity Insurance, Paul Stracke of Paychex Inc., and Ian Wall of Vertex Pharmaceuticals for a session filled with interesting projects, use cases and approaches to leveraging Oracle Data Integrator. Please read the recap by Sandrine Riley for more. Thanks to everyone who joined with us and we hope to stay connected! To hear more about our Data Integration12c products join us in an upcoming webcast to learn more. Follow us www.twitter.com/ORCLGoldenGate or goto our website at www.oracle.com/goto/dataintegration

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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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  • Splitting big request in multiple small ajax requests

    - by Ionut
    I am unsure regarding the scalability of the following model. I have no experience at all with large systems, big number of requests and so on but I'm trying to build some features considering scalability first. In my scenario there is a user page which contains data for: User's details (name, location, workplace ...) User's activity (blog posts, comments...) User statistics (rating, number of friends...) In order to show all this on the same page, for a request there will be at least 3 different database queries on the back-end. In some cases, I imagine that those queries will be running quite a wile, therefore the user experience may suffer while waiting between requests. This is why I decided to run only step 1 (User's details) as a normal request. After the response is received, two ajax requests are sent for steps 2 and 3. When those responses are received, I only place the data in the destined wrappers. For me at least this makes more sense. However there are 3 requests instead of one for every user page view. Will this affect the system on the long term? I'm assuming that this kind of approach requires more resources but is this trade of UX for resources a good dial or should I stick to one plain big request?

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  • Solving Big Problems with Oracle R Enterprise, Part I

    - by dbayard
    Abstract: This blog post will show how we used Oracle R Enterprise to tackle a customer’s big calculation problem across a big data set. Overview: Databases are great for managing large amounts of data in a central place with rigorous enterprise-level controls.  R is great for doing advanced computations.  Sometimes you need to do advanced computations on large amounts of data, subject to rigorous enterprise-level concerns.  This blog post shows how Oracle R Enterprise enables R plus the Oracle Database enabled us to do some pretty sophisticated calculations across 1 million accounts (each with many detailed records) in minutes. The problem: A financial services customer of mine has a need to calculate the historical internal rate of return (IRR) for its customers’ portfolios.  This information is needed for customer statements and the online web application.  In the past, they had solved this with a home-grown application that pulled trade and account data out of their data warehouse and ran the calculations.  But this home-grown application was not able to do this fast enough, plus it was a challenge for them to write and maintain the code that did the IRR calculation. IRR – a problem that R is good at solving: Internal Rate of Return is an interesting calculation in that in most real-world scenarios it is impractical to calculate exactly.  Rather, IRR is a calculation where approximation techniques need to be used.  In this blog post, we will discuss calculating the “money weighted rate of return” but in the actual customer proof of concept we used R to calculate both money weighted rate of returns and time weighted rate of returns.  You can learn more about the money weighted rate of returns here: http://www.wikinvest.com/wiki/Money-weighted_return First Steps- Calculating IRR in R We will start with calculating the IRR in standalone/desktop R.  In our second post, we will show how to take this desktop R function, deploy it to an Oracle Database, and make it work at real-world scale.  The first step we did was to get some sample data.  For a historical IRR calculation, you have a balances and cash flows.  In our case, the customer provided us with several accounts worth of sample data in Microsoft Excel.      The above figure shows part of the spreadsheet of sample data.  The data provides balances and cash flows for a sample account (BMV=beginning market value. FLOW=cash flow in/out of account. EMV=ending market value). Once we had the sample spreadsheet, the next step we did was to read the Excel data into R.  This is something that R does well.  R offers multiple ways to work with spreadsheet data.  For instance, one could save the spreadsheet as a .csv file.  In our case, the customer provided a spreadsheet file containing multiple sheets where each sheet provided data for a different sample account.  To handle this easily, we took advantage of the RODBC package which allowed us to read the Excel data sheet-by-sheet without having to create individual .csv files.  We wrote ourselves a little helper function called getsheet() around the RODBC package.  Then we loaded all of the sample accounts into a data.frame called SimpleMWRRData. Writing the IRR function At this point, it was time to write the money weighted rate of return (MWRR) function itself.  The definition of MWRR is easily found on the internet or if you are old school you can look in an investment performance text book.  In the customer proof, we based our calculations off the ones defined in the The Handbook of Investment Performance: A User’s Guide by David Spaulding since this is the reference book used by the customer.  (One of the nice things we found during the course of this proof-of-concept is that by using R to write our IRR functions we could easily incorporate the specific variations and business rules of the customer into the calculation.) The key thing with calculating IRR is the need to solve a complex equation with a numerical approximation technique.  For IRR, you need to find the value of the rate of return (r) that sets the Net Present Value of all the flows in and out of the account to zero.  With R, we solve this by defining our NPV function: where bmv is the beginning market value, cf is a vector of cash flows, t is a vector of time (relative to the beginning), emv is the ending market value, and tend is the ending time. Since solving for r is a one-dimensional optimization problem, we decided to take advantage of R’s optimize method (http://stat.ethz.ch/R-manual/R-patched/library/stats/html/optimize.html). The optimize method can be used to find a minimum or maximum; to find the value of r where our npv function is closest to zero, we wrapped our npv function inside the abs function and asked optimize to find the minimum.  Here is an example of using optimize: where low and high are scalars that indicate the range to search for an answer.   To test this out, we need to set values for bmv, cf, t, emv, tend, low, and high.  We will set low and high to some reasonable defaults. For example, this account had a negative 2.2% money weighted rate of return. Enhancing and Packaging the IRR function With numerical approximation methods like optimize, sometimes you will not be able to find an answer with your initial set of inputs.  To account for this, our approach was to first try to find an answer for r within a narrow range, then if we did not find an answer, try calling optimize() again with a broader range.  See the R help page on optimize()  for more details about the search range and its algorithm. At this point, we can now write a simplified version of our MWRR function.  (Our real-world version is  more sophisticated in that it calculates rate of returns for 5 different time periods [since inception, last quarter, year-to-date, last year, year before last year] in a single invocation.  In our actual customer proof, we also defined time-weighted rate of return calculations.  The beauty of R is that it was very easy to add these enhancements and additional calculations to our IRR package.)To simplify code deployment, we then created a new package of our IRR functions and sample data.  For this blog post, we only need to include our SimpleMWRR function and our SimpleMWRRData sample data.  We created the shell of the package by calling: To turn this package skeleton into something usable, at a minimum you need to edit the SimpleMWRR.Rd and SimpleMWRRData.Rd files in the \man subdirectory.  In those files, you need to at least provide a value for the “title” section. Once that is done, you can change directory to the IRR directory and type at the command-line: The myIRR package for this blog post (which has both SimpleMWRR source and SimpleMWRRData sample data) is downloadable from here: myIRR package Testing the myIRR package Here is an example of testing our IRR function once it was converted to an installable package: Calculating IRR for All the Accounts So far, we have shown how to calculate IRR for a single account.  The real-world issue is how do you calculate IRR for all of the accounts?This is the kind of situation where we can leverage the “Split-Apply-Combine” approach (see http://www.cscs.umich.edu/~crshalizi/weblog/815.html).  Given that our sample data can fit in memory, one easy approach is to use R’s “by” function.  (Other approaches to Split-Apply-Combine such as plyr can also be used.  See http://4dpiecharts.com/2011/12/16/a-quick-primer-on-split-apply-combine-problems/). Here is an example showing the use of “by” to calculate the money weighted rate of return for each account in our sample data set.  Recap and Next Steps At this point, you’ve seen the power of R being used to calculate IRR.  There were several good things: R could easily work with the spreadsheets of sample data we were given R’s optimize() function provided a nice way to solve for IRR- it was both fast and allowed us to avoid having to code our own iterative approximation algorithm R was a convenient language to express the customer-specific variations, business-rules, and exceptions that often occur in real-world calculations- these could be easily added to our IRR functions The Split-Apply-Combine technique can be used to perform calculations of IRR for multiple accounts at once. However, there are several challenges yet to be conquered at this point in our story: 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 our next blog post in this series, we will show you how Oracle R Enterprise solved these challenges.

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  • When you’re on a high, start something big

    - by BuckWoody
    Most days are pretty average – we have some highs, some lows, and just regular old work to do. But some days the sun is shining, your co-workers are especially nice, and everything just falls into place. You really *enjoy* what you do. Don’t let that moment pass. All of us have “big” projects that we need to tackle. Things that are going to take a long time, and a lot of money. Those kinds of data projects take a LOT of planning, and many times we put that off just to get to the day’s work. I’ve found that the “high” moments are the perfect time to take on these big projects. I’m more focused, and more importantly, more positive. And as the quote goes, “whether you think you can or you think you can’t, you’re probably right.” You’ll find a way to make it happen if you’re in a positive mood. Now – having those “great days” is actually something you can influence, but I’ll save that topic for a future post. I have a project to work on. :) Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

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  • Go Big or Go Special

    - by Ajarn Mark Caldwell
    Watching Shark Tank tonight and the first presentation was by Mango Mango Preserves and it highlighted an interesting contrast in business trends today and how to capitalize on opportunities.  <Spoiler Alert> Even though every one of the sharks was raving about the product samples they tried, with two of them going for second and third servings, none of them made a deal to invest in the company.</Spoiler>  In fact, one of the sharks, Kevin O’Leary, kept ripping into the owners with statements to the effect that he thinks they are headed over a financial cliff because he felt their costs were way out of line and would be their downfall if they didn’t take action to radically cut costs. He said that he had previously owned a jams and jellies business and knew the cost ratios that you had to have to make it work.  I don’t doubt he knows exactly what he’s talking about and is 100% accurate…for doing business his way, which I’ll call “Go Big”.  But there’s a whole other way to do business today that would be ideal for these ladies to pursue. As I understand it, based on his level of success in various businesses and the fact that he is even in a position to be investing in other companies, Kevin’s approach is to go mass market (Go Big) and make hundreds of millions of dollars in sales (or something along that scale) while squeezing out every ounce of cost that you can to produce an acceptable margin.  But there is a very different way of making a very successful business these days, which is all about building a passionate and loyal community of customers that are rooting for your success and even actively trying to help you succeed by promoting your product or company (Go Special).  This capitalizes on the power of social media, niche marketing, and The Long Tail.  One of the most prolific writers about capitalizing on this trend is Seth Godin, and I hope that the founders of Mango Mango pick up a couple of his books (probably Purple Cow and Tribes would be good starts) or at least read his blog.  I think the adoration expressed by all of the sharks for the product is the biggest hint that they have a remarkable product and that they are perfect for this type of business approach. Both are completely valid business models, and it may certainly be that the scale at which Kevin O’Leary wants to conduct business where he invests his money is well beyond the long tail, but that doesn’t mean that there is not still a lot of money to be made there.  I wish them the best of luck with their endeavors!

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  • Is it wise to store a big lump of json on a database row

    - by Ieyasu Sawada
    I have this project which stores product details from amazon into the database. Just to give you an idea on how big it is: [{"title":"Genetic Engineering (Opposing Viewpoints)","short_title":"Genetic Engineering ...","brand":"","condition":"","sales_rank":"7171426","binding":"Book","item_detail_url":"http://localhost/wordpress/product/?asin=0737705124","node_list":"Books > Science & Math > Biological Sciences > Biotechnology","node_category":"Books","subcat":"","model_number":"","item_url":"http://localhost/wordpress/wp-content/ecom-plugin-redirects/ecom_redirector.php?id=128","details_url":"http://localhost/wordpress/product/?asin=0737705124","large_image":"http://localhost/wordpress/wp-content/plugins/ecom/img/large-notfound.png","medium_image":"http://localhost/wordpress/wp-content/plugins/ecom/img/medium-notfound.png","small_image":"http://localhost/wordpress/wp-content/plugins/ecom/img/small-notfound.png","thumbnail_image":"http://localhost/wordpress/wp-content/plugins/ecom/img/thumbnail-notfound.png","tiny_img":"http://localhost/wordpress/wp-content/plugins/ecom/img/tiny-notfound.png","swatch_img":"http://localhost/wordpress/wp-content/plugins/ecom/img/swatch-notfound.png","total_images":"6","amount":"33.70","currency":"$","long_currency":"USD","price":"$33.70","price_type":"List Price","show_price_type":"0","stars_url":"","product_review":"","rating":"","yellow_star_class":"","white_star_class":"","rating_text":" of 5","reviews_url":"","review_label":"","reviews_label":"Read all ","review_count":"","create_review_url":"http://localhost/wordpress/wp-content/ecom-plugin-redirects/ecom_redirector.php?id=132","create_review_label":"Write a review","buy_url":"http://localhost/wordpress/wp-content/ecom-plugin-redirects/ecom_redirector.php?id=19186","add_to_cart_action":"http://localhost/wordpress/wp-content/ecom-plugin-redirects/add_to_cart.php","asin":"0737705124","status":"Only 7 left in stock.","snippet_condition":"in_stock","status_class":"ninstck","customer_images":["http://localhost/wordpress/wp-content/uploads/2013/10/ecom_images/51M2vvFvs2BL.jpg","http://localhost/wordpress/wp-content/uploads/2013/10/ecom_images/31FIM-YIUrL.jpg","http://localhost/wordpress/wp-content/uploads/2013/10/ecom_images/51M2vvFvs2BL.jpg","http://localhost/wordpress/wp-content/uploads/2013/10/ecom_images/51M2vvFvs2BL.jpg"],"disclaimer":"","item_attributes":[{"attr":"Author","value":"Greenhaven Press"},{"attr":"Binding","value":"Hardcover"},{"attr":"EAN","value":"9780737705126"},{"attr":"Edition","value":"1"},{"attr":"ISBN","value":"0737705124"},{"attr":"Label","value":"Greenhaven Press"},{"attr":"Manufacturer","value":"Greenhaven Press"},{"attr":"NumberOfItems","value":"1"},{"attr":"NumberOfPages","value":"224"},{"attr":"ProductGroup","value":"Book"},{"attr":"ProductTypeName","value":"ABIS_BOOK"},{"attr":"PublicationDate","value":"2000-06"},{"attr":"Publisher","value":"Greenhaven Press"},{"attr":"SKU","value":"G0737705124I2N00"},{"attr":"Studio","value":"Greenhaven Press"},{"attr":"Title","value":"Genetic Engineering (Opposing Viewpoints)"}],"customer_review_url":"http://localhost/wordpress/wp-content/ecom-customer-reviews/0737705124.html","flickr_results":["http://localhost/wordpress/wp-content/uploads/2013/10/ecom_images/5105560852_06c7d06f14_m.jpg"],"freebase_text":"No around the web data available yet","freebase_image":"http://localhost/wordpress/wp-content/plugins/ecom/img/freebase-notfound.jpg","ebay_related_items":[{"title":"Genetic Engineering (Introducing Issues With Opposing Viewpoints), , Good Book","image":"http://localhost/wordpress/wp-content/uploads/2013/10/ecom_images/140.jpg","url":"http://localhost/wordpress/wp-content/ecom-plugin-redirects/ecom_redirector.php?id=12165","currency_id":"$","current_price":"26.2"},{"title":"Genetic Engineering Opposing Viewpoints by DAVID BENDER - 1964 Hardcover","image":"http://localhost/wordpress/wp-content/uploads/2013/10/ecom_images/140.jpg","url":"http://localhost/wordpress/wp-content/ecom-plugin-redirects/ecom_redirector.php?id=130","currency_id":"AUD","current_price":"11.99"}],"no_follow":"rel=\"nofollow\"","new_tab":"target=\"_blank\"","related_products":[],"super_saver_shipping":"","shipping_availability":"","total_offers":"7","added_to_cart":""}] So the structure for the table is: asin title details (the product details in json) Will the performance suffer if I have to store like 10,000 products? Is there any other way of doing this? I'm thinking of the following, but the current setup is really the most convenient one since I also have to use the data on the client side: store the product details in a file. So something like ASIN123.json store the product details in one big file. (I'm guessing it will be a drag to extract data from this file) store each of the fields in the details in its own table field Thanks in advance!

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  • Pack of resources in one big file with XNA

    - by Cristian
    Is it possible to pack all the little .xnb files into one big file? Given the level of abstraction of the XNA Framework I though this would come out of the box but I can't find any well integrated solution. So far the best candidate is XnaZip but in addition to having to compile the resources in a post-build event, and a little trouble porting the game to XBOX I have to rename all the references to resources I have already implemented.

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  • It's the Freedom You Big Dummy

    <b>Daniweb:</b> "No one has given his life for Linux but certainly there have been sacrifices. But, like their armed soldier counterparts, it isn't about the sacrifice, it's the freedom you big dummy."

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  • Venez nous voir au Forum Oracle Big Data le 5 avril !

    - by Kinoa
    Le Big Data vient de plus en plus souvent au devant de la scène et vous souhaitez en apprendre davantage ? Générés à partir des réseaux sociaux, de capteurs numériques et autres équipements mobiles, les Big Data - autrement dits, d'énormes volumes de données - constituent une mine d'informations précieuses sur vos activités et les comportements de vos clients. Votre challenge aujourd’hui consiste à gérer l’acquisition, l’organisation et la compréhension de ces volumes de données non structurées, et à les intégrer dans votre système d’information. Vous avez des questions ? Ca vous parait complexe ? Alors le Forum Oracle Bid Data organisé par Oracle et Intel est fait pour vous !   Nous aborderons plusieurs points : Accélération du déploiement de Big Data par l'approche intégrée du hardware et du software Mise à disposition de tous les outils nécessaires au processus complet, de l'acquisition des données à la restitution Intégration de Big Data dans votre système d'information pour fournir aux utilisateurs la quintessence de l'information Nous vous avons concocté un programme des plus alléchant pour cette journée du 5 avril : 9h00 Accueil et remise des badges 9h30 Big Data : The Industry View. Are you ready ?Johan Hendrickx, Core Technology Director, Oracle EMEA Keynote : Big Data – Are you ready ? George Lumpkin, Vice President of DW Product Management, Oracle Corporation Acquisition des données dans votre Big Dataavec Hadoop et Oracle NoSQL Pause Organisez et structurez l'information au sein de votre Big Data avec Big Data Connectors et Oracle Data Integrator Tirez parti des analyses des données de votre Big Dataavec Oracle Endeca et Oracle Business Intelligence 13h00 Cocktail déjeunatoire Le nombre de places est limité, pensez à vous inscrire dès maintenant. Lieu :  Maison de la Chimie28 B, rue Saint Dominique 75007 Paris

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  • What makes a project big?

    - by Jonny
    Just out of curiosity what's the difference between a small, medium and large size project? Is it measured by lines of code or complexity or what? Im building a bartering system and so far have about 1000 lines of code for login/registration. Even though there's lots of LOC i wouldnt consider it a big project because its not that complex though this is my first project so im not sure. How is it measured?

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  • Some tips for working with big data models

    The main goal of this article is to present some tips to help professionals that need to work with complex, big, and hard to understand database models that anyone may came across some day. Join SQL Backup’s 35,000+ customers to compress and strengthen your backups "SQL Backup will be a REAL boost to any DBA lucky enough to use it." Jonathan Allen. Download a free trial now.

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  • Big-name School for Undergrad Students

    - by itaiferber
    As a soon-to-be graduating high school senior in the U.S., I'm going to be facing a tough decision in a few months: which college should I go to? Will it be worth it to go to Cornell or Stanford or Carnegie Mellon (assuming I get in, of course) to get a big-name computer science degree, internships, and connections with professors, while taking on massive debt; or am I better off going to SUNY Binghamton (probably the best state school in New York) and still get a pretty decent education while saving myself from over a hundred-thousand dollars worth of debt? Yes, I know questions like this has been asked before (namely here and here), but please bear with me because I haven't found an answer that fits my particular situation. I've read the two linked questions above in depth, but they haven't answered what I want to know: Yes, I understand that going to a big-name college can potentially get me connected with some wonderful professors and leaders in the field, but on average, how does that translate financially? I mean, will good connections pay off so well that I'd be easily getting rid of over a hundred-thousand dollars of debt? And how does the fact that I can get a fifth-years master's degree at Carnegie Mellon play into the equation? Will the higher degree right off the bat help me get a better-paying job just out of college, or will the extra year only put me further into debt? Not having to go to graduate school to get a comparable degree will, of course, be a great financial relief, but will getting it so early give it any greater worth? And if I go to SUNY Binghamton, which is far lesser-known than what I've considered (although if there are any alumni out there who want to share their experience, I would greatly appreciate it), would I be closing off doors that would potentially offset my short-term economic gain with long-term benefits? Essentially, is the short-term benefit overweighed by a potential long-term loss? The answers to these questions all tie in to my final college decision (again, permitting I make it to these schools), so I hope that asking the skilled and knowledgeable people of the field will help me make the right choice (if there is such a thing). Also, please note: I'm in a rather peculiar situation where I can't pay for college without taking out a bunch of loans, but will be getting little to no financial aid (likely federal or otherwise). I don't want to elaborate on this too much (so take it at face value), but this is mainly the reason I'm asking the question. Thanks a lot! It means a lot to me.

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  • Showrooming: What's the big deal?

    - by David Dorf
    There's been lots of chatter recently on how retailers will combat showrooming this holiday season.  Best Buy and Target, for example, plan to price-match certain online sites.  But from my perspective, the whole showrooming concept is overblown.  Yes, mobile phones make is easier to comparison-shop, but consumers have been doing that all along.  Retailers have to work hard to merchandise their stores with the right products at the right price with the right promotions.  Its Retail 101. Yeah ok, many websites don't have to charge tax so they have an advantage, but they also have to cover shipping costs. Brick-and-mortar stores have the opportunity to provide expertise, fit, and instant gratification all of which are pretty big advantages. I see lots of studies that claim a large percentage of shoppers are showrooming.  Now I don't do much shopping, but when I do I rarely see anyone scanning UPC codes in the aisles.  If you dig into those studies, the question is usually something like, "have you used your mobile phone to price compare while shopping in the last year."  Well yeah, I did it once -- out of the 20 shopping trips.  And by the way, the in-store price was close enough to just buy the item.  Based on casual observation and informal surveys of friends, showrooming is not the modus-operandi for today's busy shoppers. I never see people showrooming in grocery stores, and most people don't bother for fashion.  For big purchases like appliances and furniture, I bet most people do their research online before entering the store.  The cases where I've done it was to see if a promotion was in fact a good deal.  Or even to make sure the in-store price is the same as the online price for the same brand. So, if you think you're a victim of showrooming, I suggest you look at the bigger picture.  Are you providing an engaging store experience?  Are you allowing customers to shop the way they want to shop, using various touchpoints?  Are you monitoring the competition to ensure prices are competitive?  Are your promotions attracting the right customers? Hubert Jolly, CEO of Best Buy, recently commented that showrooming might just get more people into his stores. "Once customers are in our stores, they're ours to lose."

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  • SEO Consulting For Big Brand Companies - 16 Guidelines For SEO Consultants to Beat the Competition

    SEO consulting for a big brand website with tens of thousands of pages needs proven strategies that must be tailored to the specific needs of every web site. An SEO consultant, when selecting between different SEO services, must create an aggressive search engine marketing (SEM) campaign with a meticulous SEO strategy that takes all search engine optimization problems into consideration.

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  • ubuntu image size 732 mb - too big for cd

    - by memius
    i have an old pc that can't handle a boot stick install, so i have to create an actual, old fashioned boot cd. however, the image size for ubuntu 12.04 is 732mb, which is too large for cds, which can hold only 700mb. the maintainers of ubuntu 12.04 say the image size will never go over 700mb, and indeed, the download size seemed to be 689mb. Brasero says it won't burn the cd because the file is too big what's going on?

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  • Experiments in Big Data Visualization on Maps

    Experiments in Big Data Visualization on Maps Brendan Kenny and Mano Marks continue their series on using the CanvasLayer library and HTML5 APIs to visualize large amounts of data on top of Google maps. This week they look at loading Shapefiles and KML directly in the browser and using WebGL to render their content over a map. From: GoogleDevelopers Views: 0 1 ratings Time: 00:00 More in Science & Technology

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  • Performance Tuning in the Age of Big Data

    Database Administrators must now deal with large volumes of data and new forms of high-speed data analysis. If your responsibility includes performance tuning, here are the areas to focus on that will become more and more important in the age of Big Data. Total DeploymentEnjoy easy release management for your .NET apps, services, and databases with Deployment Manager. Get your free Starter edition now

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