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  • MicroTraining: Executing SSIS 2012 Packages 22 May 10:00 AM EDT (Free!)

    - by andyleonard
    I am pleased to announce the latest (free!) Linchpin People microtraining event will be held Tuesday 22 May 2012 at 10:00 AM EDT. The topic will be Executing SSIS 2012 Packages. In this presentation, I will be demonstrating several ways to execute SSIS 2012 packages. Register here ! Interested in learning about more microtraining from Linchpin People – before anyone else? Sign up for our newsletter ! :{>...(read more)

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  • MicroTraining: Executing SSIS 2012 Packages 22 May 10:00 AM EDT (Free!)

    - by andyleonard
    I am pleased to announce the latest (free!) Linchpin People microtraining event will be held Tuesday 22 May 2012 at 10:00 AM EDT. The topic will be Executing SSIS 2012 Packages. In this presentation, I will be demonstrating several ways to execute SSIS 2012 packages. Register here ! Interested in learning about more microtraining from Linchpin People – before anyone else? Sign up for our newsletter ! :{>...(read more)

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  • Data Quality Through Data Governance

    Data Quality Governance Data quality is very important to every organization, bad data cost an organization time, money, and resources that could be prevented if the proper governance was put in to place.  Data Governance Program Criteria: Support from Executive Management and all Business Units Data Stewardship Program  Cross Functional Team of Data Stewards Data Governance Committee Quality Structured Data It should go without saying but any successful project in today’s business world must get buy in from executive management and all stakeholders involved with the project. If management does not fully support a project because they see it is in there and the company’s best interest then they will remove/eliminate funding, resources and allocated time to work on the project. In essence they can render a project dead until it is official killed by the business. In addition, buy in from stake holders is also very important because they can cause delays increased spending in time, money and resources because they do not support a project. Data Stewardship programs are administered by a data steward manager who primary focus is to support, train and manage a cross functional data stewards team. A cross functional team of data stewards are pulled from various departments act to ensure that all systems work to ensure that an organization’s goals are achieved. Typically, data stewards are subject matter experts that act as mediators between their respective departments and IT. Data Quality Procedures Data Governance Committees are composed of data stewards, Upper management, IT Leadership and various subject matter experts depending on a company. The primary goal of this committee is to define strategic goals, coordinate activities, set data standards and offer data guidelines for the business. Data Quality Policies In 1997, Claudia Imhoff defined a Data Stewardship’s responsibility as to approve business naming standards, develop consistent data definitions, determine data aliases, develop standard calculations and derivations, document the business rules of the corporation, monitor the quality of the data in the data warehouse, define security requirements, and so forth. She further explains data stewards responsible for creating and enforcing polices on the following but not limited to issues. Resolving Data Integration Issues Determining Data Security Documenting Data Definitions, Calculations, Summarizations, etc. Maintaining/Updating Business Rules Analyzing and Improving Data Quality

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  • T-SQL Tuesday: Aggregations in SSIS

    - by andyleonard
    Introduction Jes Borland ( Blog | @grrl_geek ) is hosting this month's T-SQL Tuesday - started by SQLBlog's own Adam Machanic ( Blog | @AdamMachanic ) - and it is about aggregation. I thought I'd show a couple ways to do aggregation using SSIS. The Aggregate Transformation in SSIS The Aggregate transform in SSIS is fast . I built an SSIS package (AggregateScripts.dtsx) with two Data Flow Tasks (Using the Aggregate Transform and Using a Script Component). Using the Aggregate Transform looks like this:...(read more)

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  • SQL SERVER – Advanced Data Quality Services with Melissa Data – Azure Data Market

    - by pinaldave
    There has been much fanfare over the new SQL Server 2012, and especially around its new companion product Data Quality Services (DQS). Among the many new features is the addition of this integrated knowledge-driven product that enables data stewards everywhere to profile, match, and cleanse data. In addition to the homegrown rules that data stewards can design and implement, there are also connectors to third party providers that are hosted in the Azure Datamarket marketplace.  In this review, I leverage SQL Server 2012 Data Quality Services, and proceed to subscribe to a third party data cleansing product through the Datamarket to showcase this unique capability. Crucial Questions For the purposes of the review, I used a database I had in an Excel spreadsheet with name and address information. Upon a cursory inspection, there are miscellaneous problems with these records; some addresses are missing ZIP codes, others missing a city, and some records are slightly misspelled or have unparsed suites. With DQS, I can easily add a knowledge base to help standardize my values, such as for state abbreviations. But how do I know that my address is correct? And if my address is not correct, what should it be corrected to? The answer lies in a third party knowledge base by the acknowledged USPS certified address accuracy experts at Melissa Data. Reference Data Services Within DQS there is a handy feature to actually add reference data from many different third-party Reference Data Services (RDS) vendors. DQS simplifies the processes of cleansing, standardizing, and enriching data through custom rules and through service providers from the Azure Datamarket. A quick jump over to the Datamarket site shows me that there are a handful of providers that offer data directly through Data Quality Services. Upon subscribing to these services, one can attach a DQS domain or composite domain (fields in a record) to a reference data service provider, and begin using it to cleanse, standardize, and enrich that data. Besides what I am looking for (address correction and enrichment), it is possible to subscribe to a host of other services including geocoding, IP address reference, phone checking and enrichment, as well as name parsing, standardization, and genderization.  These capabilities extend the data quality that DQS has natively by quite a bit. For my current address correction review, I needed to first sign up to a reference data provider on the Azure Data Market site. For this example, I used Melissa Data’s Address Check Service. They offer free one-month trials, so if you wish to follow along, or need to add address quality to your own data, I encourage you to sign up with them. Once I subscribed to the desired Reference Data Provider, I navigated my browser to the Account Keys within My Account to view the generated account key, which I then inserted into the DQS Client – Configuration under the Administration area. Step by Step to Guide That was all it took to hook in the subscribed provider -Melissa Data- directly to my DQS Client. The next step was for me to attach and map in my Reference Data from the newly acquired reference data provider, to a domain in my knowledge base. On the DQS Client home screen, I selected “New Knowledge Base” under Knowledge Base Management on the left-hand side of the home screen. Under New Knowledge Base, I typed a Name and description of my new knowledge base, then proceeded to the Domain Management screen. Here I established a series of domains (fields) and then linked them all together as a composite domain (record set). Using the Create Domain button, I created the following domains according to the fields in my incoming data: Name Address Suite City State Zip I added a Suite column in my domain because Melissa Data has the ability to return missing Suites based on last name or company. And that’s a great benefit of using these third party providers, as they have data that the data steward would not normally have access to. The bottom line is, with these third party data providers, I can actually improve my data. Next, I created a composite domain (fulladdress) and added the (field) domains into the composite domain. This essentially groups our address fields together in a record to facilitate the full address cleansing they perform. I then selected my newly created composite domain and under the Reference Data tab, added my third party reference data provider –Melissa Data’s Address Check- and mapped in each domain that I had to the provider’s Schema. Now that my composite domain has been married to the Reference Data service, I can take the newly published knowledge base and create a project to cleanse and enrich my data. My next task was to create a new Data Quality project, mapping in my data source and matching it to the appropriate domain column, and then kick off the verification process. It took just a few minutes with some progress indicators indicating that it was working. When the process concluded, there was a helpful set of tabs that place the response records into categories: suggested; new; invalid; corrected (automatically); and correct. Accepting the suggestions provided by  Melissa Data allowed me to clean up all the records and flag the invalid ones. It is very apparent that DQS makes address data quality simplistic for any IT professional. Final Note As I have shown, DQS makes data quality very easy. Within minutes I was able to set up a data cleansing and enrichment routine within my data quality project, and ensure that my address data was clean, verified, and standardized against real reference data. As reviewed here, it’s easy to see how both SQL Server 2012 and DQS work to take what used to require a highly skilled developer, and empower an average business or database person to consume external services and clean data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL Utility, T SQL, Technology Tagged: DQS

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  • SQL Server Configuration timeouts - and a workaround [SSIS]

    - by jamiet
    Ever since I started writing SSIS packages back in 2004 I have opted to store configurations in .dtsConfig (.i.e. XML) files rather than in a SQL Server table (aka SQL Server Configurations) however recently I inherited some packages that used SQL Server Configurations and thus had to immerse myself in their murky little world. To all the people that have ever gone onto the SSIS forum and asked questions about ambiguous behaviour of SQL Server Configurations I now say this... I feel your pain! The biggest problem I have had was in dealing with the change to the order in which configurations get applied that came about in SSIS 2008. Those changes are detailed on MSDN at SSIS Package Configurations however the pertinent bits are: As the utility loads and runs the package, events occur in the following order: The dtexec utility loads the package. The utility applies the configurations that were specified in the package at design time and in the order that is specified in the package. (The one exception to this is the Parent Package Variables configurations. The utility applies these configurations only once and later in the process.) The utility then applies any options that you specified on the command line. The utility then reloads the configurations that were specified in the package at design time and in the order specified in the package. (Again, the exception to this rule is the Parent Package Variables configurations). The utility uses any command-line options that were specified to reload the configurations. Therefore, different values might be reloaded from a different location. The utility applies the Parent Package Variable configurations. The utility runs the package. To understand how these steps differ from SSIS 2005 I recommend reading Doug Laudenschlager’s blog post Understand how SSIS package configurations are applied. The very nature of SQL Server Configurations means that the Connection String for the database holding the configuration values needs to be supplied from the command-line. Typically then the call to execute your package resembles this: dtexec /FILE Package.dtsx /SET "\Package.Connections[SSISConfigurations].Properties[ConnectionString]";"\"Data Source=SomeServer;Initial Catalog=SomeDB;Integrated Security=SSPI;\"", The problem then is that, as per the steps above, the package will (1) attempt to apply all configurations using the Connection String stored in the package for the "SSISConfigurations" Connection Manager before then (2) applying the Connection String from the command-line and then (3) apply the same configurations all over again. In the packages that I inherited that first attempt to apply the configurations would timeout (not unexpected); I had 8 SQL Server Configurations in the package and thus the package was waiting for 2 minutes until all the Configurations timed out (i.e. 15seconds per Configuration) - in a package that only executes for ~8seconds when it gets to do its actual work a delay of 2minutes was simply unacceptable. We had three options in how to deal with this: Get rid of the use of SQL Server configurations and use .dtsConfig files instead Edit the packages when they get deployed Change the timeout on the "SSISConfigurations" Connection Manager #1 was my preferred choice but, for reasons I explain below*, wasn't an option in this particular instance. #2 was discounted out of hand because it negates the point of using Configurations in the first place. This left us with #3 - change the timeout on the Connection Manager. This is done by going into the properties of the Connection Manager, opening the "All" tab and changing the Connect Timeout property to some suitable value (in the screenshot below I chose 2 seconds). This change meant that the attempts to apply the SQL Server configurations timed out in 16 seconds rather than two minutes; clearly this isn't an optimum solution but its certainly better than it was. So there you have it - if you are having problems with SQL Server configuration timeouts within SSIS try changing the timeout of the Connection Manager. Better still - don't bother using SQL Server Configuration in the first place. Even better - install RC0 of SQL Server 2012 to start leveraging SSIS parameters and leave the nasty old world of configurations behind you. @Jamiet * Basically, we are leveraging a SSIS execution/logging framework in which the client had invested a lot of resources and SQL Server Configurations are an integral part of that.

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  • SQL SERVER – SSIS Look Up Component – Cache Mode – Notes from the Field #028

    - by Pinal Dave
    [Notes from Pinal]: Lots of people think that SSIS is all about arranging various operations together in one logical flow. Well, the understanding is absolutely correct, but the implementation of the same is not as easy as it seems. Similarly most of the people think lookup component is just component which does look up for additional information and does not pay much attention to it. Due to the same reason they do not pay attention to the same and eventually get very bad performance. Linchpin People are database coaches and wellness experts for a data driven world. In this 28th episode of the Notes from the Fields series database expert Tim Mitchell (partner at Linchpin People) shares very interesting conversation related to how to write a good lookup component with Cache Mode. In SQL Server Integration Services, the lookup component is one of the most frequently used tools for data validation and completion.  The lookup component is provided as a means to virtually join one set of data to another to validate and/or retrieve missing values.  Properly configured, it is reliable and reasonably fast. Among the many settings available on the lookup component, one of the most critical is the cache mode.  This selection will determine whether and how the distinct lookup values are cached during package execution.  It is critical to know how cache modes affect the result of the lookup and the performance of the package, as choosing the wrong setting can lead to poorly performing packages, and in some cases, incorrect results. Full Cache The full cache mode setting is the default cache mode selection in the SSIS lookup transformation.  Like the name implies, full cache mode will cause the lookup transformation to retrieve and store in SSIS cache the entire set of data from the specified lookup location.  As a result, the data flow in which the lookup transformation resides will not start processing any data buffers until all of the rows from the lookup query have been cached in SSIS. The most commonly used cache mode is the full cache setting, and for good reason.  The full cache setting has the most practical applications, and should be considered the go-to cache setting when dealing with an untested set of data. With a moderately sized set of reference data, a lookup transformation using full cache mode usually performs well.  Full cache mode does not require multiple round trips to the database, since the entire reference result set is cached prior to data flow execution. There are a few potential gotchas to be aware of when using full cache mode.  First, you can see some performance issues – memory pressure in particular – when using full cache mode against large sets of reference data.  If the table you use for the lookup is very large (either deep or wide, or perhaps both), there’s going to be a performance cost associated with retrieving and caching all of that data.  Also, keep in mind that when doing a lookup on character data, full cache mode will always do a case-sensitive (and in some cases, space-sensitive) string comparison even if your database is set to a case-insensitive collation.  This is because the in-memory lookup uses a .NET string comparison (which is case- and space-sensitive) as opposed to a database string comparison (which may be case sensitive, depending on collation).  There’s a relatively easy workaround in which you can use the UPPER() or LOWER() function in the pipeline data and the reference data to ensure that case differences do not impact the success of your lookup operation.  Again, neither of these present a reason to avoid full cache mode, but should be used to determine whether full cache mode should be used in a given situation. Full cache mode is ideally useful when one or all of the following conditions exist: The size of the reference data set is small to moderately sized The size of the pipeline data set (the data you are comparing to the lookup table) is large, is unknown at design time, or is unpredictable Each distinct key value(s) in the pipeline data set is expected to be found multiple times in that set of data Partial Cache When using the partial cache setting, lookup values will still be cached, but only as each distinct value is encountered in the data flow.  Initially, each distinct value will be retrieved individually from the specified source, and then cached.  To be clear, this is a row-by-row lookup for each distinct key value(s). This is a less frequently used cache setting because it addresses a narrower set of scenarios.  Because each distinct key value(s) combination requires a relational round trip to the lookup source, performance can be an issue, especially with a large pipeline data set to be compared to the lookup data set.  If you have, for example, a million records from your pipeline data source, you have the potential for doing a million lookup queries against your lookup data source (depending on the number of distinct values in the key column(s)).  Therefore, one has to be keenly aware of the expected row count and value distribution of the pipeline data to safely use partial cache mode. Using partial cache mode is ideally suited for the conditions below: The size of the data in the pipeline (more specifically, the number of distinct key column) is relatively small The size of the lookup data is too large to effectively store in cache The lookup source is well indexed to allow for fast retrieval of row-by-row values No Cache As you might guess, selecting no cache mode will not add any values to the lookup cache in SSIS.  As a result, every single row in the pipeline data set will require a query against the lookup source.  Since no data is cached, it is possible to save a small amount of overhead in SSIS memory in cases where key values are not reused.  In the real world, I don’t see a lot of use of the no cache setting, but I can imagine some edge cases where it might be useful. As such, it’s critical to know your data before choosing this option.  Obviously, performance will be an issue with anything other than small sets of data, as the no cache setting requires row-by-row processing of all of the data in the pipeline. I would recommend considering the no cache mode only when all of the below conditions are true: The reference data set is too large to reasonably be loaded into SSIS memory The pipeline data set is small and is not expected to grow There are expected to be very few or no duplicates of the key values(s) in the pipeline data set (i.e., there would be no benefit from caching these values) Conclusion The cache mode, an often-overlooked setting on the SSIS lookup component, represents an important design decision in your SSIS data flow.  Choosing the right lookup cache mode directly impacts the fidelity of your results and the performance of package execution.  Know how this selection impacts your ETL loads, and you’ll end up with more reliable, faster packages. If you want me to take a look at your server and its settings, or if your server is facing any issue we can Fix Your SQL Server. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: Notes from the Field, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL Tagged: SSIS

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  • SQL SERVER – Parsing SSIS Catalog Messages – Notes from the Field #030

    - by Pinal Dave
    [Note from Pinal]: This is a new episode of Notes from the Field series. SQL Server Integration Service (SSIS) is one of the most key essential part of the entire Business Intelligence (BI) story. It is a platform for data integration and workflow applications. The tool may also be used to automate maintenance of SQL Server databases and updates to multidimensional cube data. In this episode of the Notes from the Field series I requested SSIS Expert Andy Leonard to discuss one of the most interesting concepts of SSIS Catalog Messages. There are plenty of interesting and useful information captured in the SSIS catalog and we will learn together how to explore the same. The SSIS Catalog captures a lot of cool information by default. Here’s a query I use to parse messages from the catalog.operation_messages table in the SSISDB database, where the logged messages are stored. This query is set up to parse a default message transmitted by the Lookup Transformation. It’s one of my favorite messages in the SSIS log because it gives me excellent information when I’m tuning SSIS data flows. The message reads similar to: Data Flow Task:Information: The Lookup processed 4485 rows in the cache. The processing time was 0.015 seconds. The cache used 1376895 bytes of memory. The query: USE SSISDB GO DECLARE @MessageSourceType INT = 60 DECLARE @StartOfIDString VARCHAR(100) = 'The Lookup processed ' DECLARE @ProcessingTimeString VARCHAR(100) = 'The processing time was ' DECLARE @CacheUsedString VARCHAR(100) = 'The cache used ' DECLARE @StartOfIDSearchString VARCHAR(100) = '%' + @StartOfIDString + '%' DECLARE @ProcessingTimeSearchString VARCHAR(100) = '%' + @ProcessingTimeString + '%' DECLARE @CacheUsedSearchString VARCHAR(100) = '%' + @CacheUsedString + '%' SELECT operation_id , SUBSTRING(MESSAGE, (PATINDEX(@StartOfIDSearchString,MESSAGE) + LEN(@StartOfIDString) + 1), ((CHARINDEX(' ', MESSAGE, PATINDEX(@StartOfIDSearchString,MESSAGE) + LEN(@StartOfIDString) + 1)) - (PATINDEX(@StartOfIDSearchString, MESSAGE) + LEN(@StartOfIDString) + 1))) AS LookupRowsCount , SUBSTRING(MESSAGE, (PATINDEX(@ProcessingTimeSearchString,MESSAGE) + LEN(@ProcessingTimeString) + 1), ((CHARINDEX(' ', MESSAGE, PATINDEX(@ProcessingTimeSearchString,MESSAGE) + LEN(@ProcessingTimeString) + 1)) - (PATINDEX(@ProcessingTimeSearchString, MESSAGE) + LEN(@ProcessingTimeString) + 1))) AS LookupProcessingTime , CASE WHEN (CONVERT(numeric(3,3),SUBSTRING(MESSAGE, (PATINDEX(@ProcessingTimeSearchString,MESSAGE) + LEN(@ProcessingTimeString) + 1), ((CHARINDEX(' ', MESSAGE, PATINDEX(@ProcessingTimeSearchString,MESSAGE) + LEN(@ProcessingTimeString) + 1)) - (PATINDEX(@ProcessingTimeSearchString, MESSAGE) + LEN(@ProcessingTimeString) + 1))))) = 0 THEN 0 ELSE CONVERT(bigint,SUBSTRING(MESSAGE, (PATINDEX(@StartOfIDSearchString,MESSAGE) + LEN(@StartOfIDString) + 1), ((CHARINDEX(' ', MESSAGE, PATINDEX(@StartOfIDSearchString,MESSAGE) + LEN(@StartOfIDString) + 1)) - (PATINDEX(@StartOfIDSearchString, MESSAGE) + LEN(@StartOfIDString) + 1)))) / CONVERT(numeric(3,3),SUBSTRING(MESSAGE, (PATINDEX(@ProcessingTimeSearchString,MESSAGE) + LEN(@ProcessingTimeString) + 1), ((CHARINDEX(' ', MESSAGE, PATINDEX(@ProcessingTimeSearchString,MESSAGE) + LEN(@ProcessingTimeString) + 1)) - (PATINDEX(@ProcessingTimeSearchString, MESSAGE) + LEN(@ProcessingTimeString) + 1)))) END AS LookupRowsPerSecond , SUBSTRING(MESSAGE, (PATINDEX(@CacheUsedSearchString,MESSAGE) + LEN(@CacheUsedString) + 1), ((CHARINDEX(' ', MESSAGE, PATINDEX(@CacheUsedSearchString,MESSAGE) + LEN(@CacheUsedString) + 1)) - (PATINDEX(@CacheUsedSearchString, MESSAGE) + LEN(@CacheUsedString) + 1))) AS LookupBytesUsed ,CASE WHEN (CONVERT(bigint,SUBSTRING(MESSAGE, (PATINDEX(@StartOfIDSearchString,MESSAGE) + LEN(@StartOfIDString) + 1), ((CHARINDEX(' ', MESSAGE, PATINDEX(@StartOfIDSearchString,MESSAGE) + LEN(@StartOfIDString) + 1)) - (PATINDEX(@StartOfIDSearchString, MESSAGE) + LEN(@StartOfIDString) + 1)))))= 0 THEN 0 ELSE CONVERT(bigint,SUBSTRING(MESSAGE, (PATINDEX(@CacheUsedSearchString,MESSAGE) + LEN(@CacheUsedString) + 1), ((CHARINDEX(' ', MESSAGE, PATINDEX(@CacheUsedSearchString,MESSAGE) + LEN(@CacheUsedString) + 1)) - (PATINDEX(@CacheUsedSearchString, MESSAGE) + LEN(@CacheUsedString) + 1)))) / CONVERT(bigint,SUBSTRING(MESSAGE, (PATINDEX(@StartOfIDSearchString,MESSAGE) + LEN(@StartOfIDString) + 1), ((CHARINDEX(' ', MESSAGE, PATINDEX(@StartOfIDSearchString,MESSAGE) + LEN(@StartOfIDString) + 1)) - (PATINDEX(@StartOfIDSearchString, MESSAGE) + LEN(@StartOfIDString) + 1)))) END AS LookupBytesPerRow FROM [catalog].[operation_messages] WHERE message_source_type = @MessageSourceType AND MESSAGE LIKE @StartOfIDSearchString GO Note that you have to set some parameter values: @MessageSourceType [int] – represents the message source type value from the following results: Value     Description 10           Entry APIs, such as T-SQL and CLR Stored procedures 20           External process used to run package (ISServerExec.exe) 30           Package-level objects 40           Control Flow tasks 50           Control Flow containers 60           Data Flow task 70           Custom execution message Note: Taken from Reza Rad’s (excellent!) helper.MessageSourceType table found here. @StartOfIDString [VarChar(100)] – use this to uniquely identify the message field value you wish to parse. In this case, the string ‘The Lookup processed ‘ identifies all the Lookup Transformation messages I desire to parse. @ProcessingTimeString [VarChar(100)] – this parameter is message-specific. I use this parameter to specifically search the message field value for the beginning of the Lookup Processing Time value. For this execution, I use the string ‘The processing time was ‘. @CacheUsedString [VarChar(100)] – this parameter is also message-specific. I use this parameter to specifically search the message field value for the beginning of the Lookup Cache  Used value. It returns the memory used, in bytes. For this execution, I use the string ‘The cache used ‘. The other parameters are built from variations of the parameters listed above. The query parses the values into text. The string values are converted to numeric values for ratio calculations; LookupRowsPerSecond and LookupBytesPerRow. Since ratios involve division, CASE statements check for denominators that equal 0. Here are the results in an SSMS grid: This is not the only way to retrieve this information. And much of the code lends itself to conversion to functions. If there is interest, I will share the functions in an upcoming post. If you want to get started with SSIS with the help of experts, read more over at Fix Your SQL Server. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: Notes from the Field, PostADay, SQL, SQL Authority, SQL Backup and Restore, SQL Query, SQL Server, SQL Tips and Tricks, T SQL Tagged: SSIS

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  • SSIS: Deploying OLAP cubes using C# script tasks and AMO

    - by DrJohn
    As part of the continuing series on Building dynamic OLAP data marts on-the-fly, this blog entry will focus on how to automate the deployment of OLAP cubes using SQL Server Integration Services (SSIS) and Analysis Services Management Objects (AMO). OLAP cube deployment is usually done using the Analysis Services Deployment Wizard. However, this option was dismissed for a variety of reasons. Firstly, invoking external processes from SSIS is fraught with problems as (a) it is not always possible to ensure SSIS waits for the external program to terminate; (b) we cannot log the outcome properly and (c) it is not always possible to control the server's configuration to ensure the executable works correctly. Another reason for rejecting the Deployment Wizard is that it requires the 'answers' to be written into four XML files. These XML files record the three things we need to change: the name of the server, the name of the OLAP database and the connection string to the data mart. Although it would be reasonably straight forward to change the content of the XML files programmatically, this adds another set of complication and level of obscurity to the overall process. When I first investigated the possibility of using C# to deploy a cube, I was surprised to find that there are no other blog entries about the topic. I can only assume everyone else is happy with the Deployment Wizard! SSIS "forgets" assembly references If you build your script task from scratch, you will have to remember how to overcome one of the major annoyances of working with SSIS script tasks: the forgetful nature of SSIS when it comes to assembly references. Basically, you can go through the process of adding an assembly reference using the Add Reference dialog, but when you close the script window, SSIS "forgets" the assembly reference so the script will not compile. After repeating the operation several times, you will find that SSIS only remembers the assembly reference when you specifically press the Save All icon in the script window. This problem is not unique to the AMO assembly and has certainly been a "feature" since SQL Server 2005, so I am not amazed it is still present in SQL Server 2008 R2! Sample Package So let's take a look at the sample SSIS package I have provided which can be downloaded from here: DeployOlapCubeExample.zip  Below is a screenshot after a successful run. Connection Managers The package has three connection managers: AsDatabaseDefinitionFile is a file connection manager pointing to the .asdatabase file you wish to deploy. Note that this can be found in the bin directory of you OLAP database project once you have clicked the "Build" button in Visual Studio TargetOlapServerCS is an Analysis Services connection manager which identifies both the deployment server and the target database name. SourceDataMart is an OLEDB connection manager pointing to the data mart which is to act as the source of data for your cube. This will be used to replace the connection string found in your .asdatabase file Once you have configured the connection managers, the sample should run and deploy your OLAP database in a few seconds. Of course, in a production environment, these connection managers would be associated with package configurations or set at runtime. When you run the sample, you should see that the script logs its activity to the output screen (see screenshot above). If you configure logging for the package, then these messages will also appear in your SSIS logging. Sample Code Walkthrough Next let's walk through the code. The first step is to parse the connection string provided by the TargetOlapServerCS connection manager and obtain the name of both the target OLAP server and also the name of the OLAP database. Note that the target database does not have to exist to be referenced in an AS connection manager, so I am using this as a convenient way to define both properties. We now connect to the server and check for the existence of the OLAP database. If it exists, we drop the database so we can re-deploy. svr.Connect(olapServerName); if (svr.Connected) { // Drop the OLAP database if it already exists Database db = svr.Databases.FindByName(olapDatabaseName); if (db != null) { db.Drop(); } // rest of script } Next we start building the XMLA command that will actually perform the deployment. Basically this is a small chuck of XML which we need to wrap around the large .asdatabase file generated by the Visual Studio build process. // Start generating the main part of the XMLA command XmlDocument xmlaCommand = new XmlDocument(); xmlaCommand.LoadXml(string.Format("<Batch Transaction='false' xmlns='http://schemas.microsoft.com/analysisservices/2003/engine'><Alter AllowCreate='true' ObjectExpansion='ExpandFull'><Object><DatabaseID>{0}</DatabaseID></Object><ObjectDefinition/></Alter></Batch>", olapDatabaseName));  Next we need to merge two XML files which we can do by simply using setting the InnerXml property of the ObjectDefinition node as follows: // load OLAP Database definition from .asdatabase file identified by connection manager XmlDocument olapCubeDef = new XmlDocument(); olapCubeDef.Load(Dts.Connections["AsDatabaseDefinitionFile"].ConnectionString); // merge the two XML files by obtain a reference to the ObjectDefinition node oaRootNode.InnerXml = olapCubeDef.InnerXml;   One hurdle I had to overcome was removing detritus from the .asdabase file left by the Visual Studio build. Through an iterative process, I found I needed to remove several nodes as they caused the deployment to fail. The XMLA error message read "Cannot set read-only node: CreatedTimestamp" or similar. In comparing the XMLA generated with by the Deployment Wizard with that generated by my code, these read-only nodes were missing, so clearly I just needed to strip them out. This was easily achieved using XPath to find the relevant XML nodes, of which I show one example below: foreach (XmlNode node in rootNode.SelectNodes("//ns1:CreatedTimestamp", nsManager)) { node.ParentNode.RemoveChild(node); } Now we need to change the database name in both the ID and Name nodes using code such as: XmlNode databaseID = xmlaCommand.SelectSingleNode("//ns1:Database/ns1:ID", nsManager); if (databaseID != null) databaseID.InnerText = olapDatabaseName; Finally we need to change the connection string to point at the relevant data mart. Again this is easily achieved using XPath to search for the relevant nodes and then replace the content of the node with the new name or connection string. XmlNode connectionStringNode = xmlaCommand.SelectSingleNode("//ns1:DataSources/ns1:DataSource/ns1:ConnectionString", nsManager); if (connectionStringNode != null) { connectionStringNode.InnerText = Dts.Connections["SourceDataMart"].ConnectionString; } Finally we need to perform the deployment using the Execute XMLA command and check the returned XmlaResultCollection for errors before setting the Dts.TaskResult. XmlaResultCollection oResults = svr.Execute(xmlaCommand.InnerXml);  // check for errors during deployment foreach (Microsoft.AnalysisServices.XmlaResult oResult in oResults) { foreach (Microsoft.AnalysisServices.XmlaMessage oMessage in oResult.Messages) { if ((oMessage.GetType().Name == "XmlaError")) { FireError(oMessage.Description); HadError = true; } } } If you are not familiar with XML programming, all this may all seem a bit daunting, but perceiver as the sample code is pretty short. If you would like the script to process the OLAP database, simply uncomment the lines in the vicinity of Process method. Of course, you can extend the script to perform your own custom processing and to even synchronize the database to a front-end server. Personally, I like to keep the deployment and processing separate as the code can become overly complex for support staff.If you want to know more, come see my session at the forthcoming SQLBits conference.

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  • Big Data – Is Big Data Relevant to me? – Big Data Questionnaires – Guest Post by Vinod Kumar

    - by Pinal Dave
    This guest post is by Vinod Kumar. Vinod Kumar has worked with SQL Server extensively since joining the industry over a decade ago. Working on various versions of SQL Server 7.0, Oracle 7.3 and other database technologies – he now works with the Microsoft Technology Center (MTC) as a Technology Architect. Let us read the blog post in Vinod’s own voice. I think the series from Pinal is a good one for anyone planning to start on Big Data journey from the basics. In my daily customer interactions this buzz of “Big Data” always comes up, I react generally saying – “Sir, do you really have a ‘Big Data’ problem or do you have a big Data problem?” Generally, there is a silence in the air when I ask this question. Data is everywhere in organizations – be it big data, small data, all data and for few it is bad data which is same as no data :). Wow, don’t discount me as someone who opposes “Big Data”, I am a big supporter as much as I am a critic of the abuse of this term by the people. In this post, I wanted to let my mind flow so that you can also think in the direction I want you to see these concepts. In any case, this is not an exhaustive dump of what is in my mind – but you will surely get the drift how I am going to question Big Data terms from customers!!! Is Big Data Relevant to me? Many of my customers talk to me like blank whiteboard with no idea – “why Big Data”. They want to jump into the bandwagon of technology and they want to decipher insights from their unexplored data a.k.a. unstructured data with structured data. So what are these industry scenario’s that come to mind? Here are some of them: Financials Fraud detection: Banks and Credit cards are monitoring your spending habits on real-time basis. Customer Segmentation: applies in every industry from Banking to Retail to Aviation to Utility and others where they deal with end customer who consume their products and services. Customer Sentiment Analysis: Responding to negative brand perception on social or amplify the positive perception. Sales and Marketing Campaign: Understand the impact and get closer to customer delight. Call Center Analysis: attempt to take unstructured voice recordings and analyze them for content and sentiment. Medical Reduce Re-admissions: How to build a proactive follow-up engagements with patients. Patient Monitoring: How to track Inpatient, Out-Patient, Emergency Visits, Intensive Care Units etc. Preventive Care: Disease identification and Risk stratification is a very crucial business function for medical. Claims fraud detection: There is no precise dollars that one can put here, but this is a big thing for the medical field. Retail Customer Sentiment Analysis, Customer Care Centers, Campaign Management. Supply Chain Analysis: Every sensors and RFID data can be tracked for warehouse space optimization. Location based marketing: Based on where a check-in happens retail stores can be optimize their marketing. Telecom Price optimization and Plans, Finding Customer churn, Customer loyalty programs Call Detail Record (CDR) Analysis, Network optimizations, User Location analysis Customer Behavior Analysis Insurance Fraud Detection & Analysis, Pricing based on customer Sentiment Analysis, Loyalty Management Agents Analysis, Customer Value Management This list can go on to other areas like Utility, Manufacturing, Travel, ITES etc. So as you can see, there are obviously interesting use cases for each of these industry verticals. These are just representative list. Where to start? A lot of times I try to quiz customers on a number of dimensions before starting a Big Data conversation. Are you getting the data you need the way you want it and in a timely manner? Can you get in and analyze the data you need? How quickly is IT to respond to your BI Requests? How easily can you get at the data that you need to run your business/department/project? How are you currently measuring your business? Can you get the data you need to react WITHIN THE QUARTER to impact behaviors to meet your numbers or is it always “rear-view mirror?” How are you measuring: The Brand Customer Sentiment Your Competition Your Pricing Your performance Supply Chain Efficiencies Predictive product / service positioning What are your key challenges of driving collaboration across your global business?  What the challenges in innovation? What challenges are you facing in getting more information out of your data? Note: Garbage-in is Garbage-out. Hold good for all reporting / analytics requirements Big Data POCs? A number of customers get into the realm of setting a small team to work on Big Data – well it is a great start from an understanding point of view, but I tend to ask a number of other questions to such customers. Some of these common questions are: To what degree is your advanced analytics (natural language processing, sentiment analysis, predictive analytics and classification) paired with your Big Data’s efforts? Do you have dedicated resources exploring the possibilities of advanced analytics in Big Data for your business line? Do you plan to employ machine learning technology while doing Advanced Analytics? How is Social Media being monitored in your organization? What is your ability to scale in terms of storage and processing power? Do you have a system in place to sort incoming data in near real time by potential value, data quality, and use frequency? Do you use event-driven architecture to manage incoming data? Do you have specialized data services that can accommodate different formats, security, and the management requirements of multiple data sources? Is your organization currently using or considering in-memory analytics? To what degree are you able to correlate data from your Big Data infrastructure with that from your enterprise data warehouse? Have you extended the role of Data Stewards to include ownership of big data components? Do you prioritize data quality based on the source system (that is Facebook/Twitter data has lower quality thresholds than radio frequency identification (RFID) for a tracking system)? Do your retention policies consider the different legal responsibilities for storing Big Data for a specific amount of time? Do Data Scientists work in close collaboration with Data Stewards to ensure data quality? How is access to attributes of Big Data being given out in the organization? Are roles related to Big Data (Advanced Analyst, Data Scientist) clearly defined? How involved is risk management in the Big Data governance process? Is there a set of documented policies regarding Big Data governance? Is there an enforcement mechanism or approach to ensure that policies are followed? Who is the key sponsor for your Big Data governance program? (The CIO is best) Do you have defined policies surrounding the use of social media data for potential employees and customers, as well as the use of customer Geo-location data? How accessible are complex analytic routines to your user base? What is the level of involvement with outside vendors and third parties in regard to the planning and execution of Big Data projects? What programming technologies are utilized by your data warehouse/BI staff when working with Big Data? These are some of the important questions I ask each customer who is actively evaluating Big Data trends for their organizations. These questions give you a sense of direction where to start, what to use, how to secure, how to analyze and more. Sign off Any Big data is analysis is incomplete without a compelling story. The best way to understand this is to watch Hans Rosling – Gapminder (2:17 to 6:06) videos about the third world myths. Don’t get overwhelmed with the Big Data buzz word, the destination to what your data speaks is important. In this blog post, we did not particularly look at any Big Data technologies. This is a set of questionnaire one needs to keep in mind as they embark their journey of Big Data. I did write some of the basics in my blog: Big Data – Big Hype yet Big Opportunity. Do let me know if these questions make sense?  Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Querying the SSIS Catalog? Here’s a handy query!

    - by jamiet
    I’ve been working on a SQL Server Integration Services (SSIS) solution for about 6 months now and I’ve learnt many many things that I intend to share on this blog just as soon as I get the time. Here’s a very short starter-for-ten… I’ve found the following query to be utterly invaluable when interrogating the SSIS Catalog to discover what is going on in my executions: SELECT event_message_id,MESSAGE,package_name,event_name,message_source_name,package_path,execution_path,message_type,message_source_typeFROM   (       SELECT  em.*       FROM    SSISDB.catalog.event_messages em       WHERE   em.operation_id = (SELECT MAX(execution_id) FROM SSISDB.catalog.executions)           AND event_name NOT LIKE '%Validate%'       )q/* Put in whatever WHERE predicates you might like*/--WHERE event_name = 'OnError'--WHERE package_name = 'Package.dtsx'--WHERE execution_path LIKE '%<some executable>%'ORDER BY message_time DESC Know it. Learn it. Love it. @jamiet

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  • Big Data – Basics of Big Data Architecture – Day 4 of 21

    - by Pinal Dave
    In yesterday’s blog post we understood how Big Data evolution happened. Today we will understand basics of the Big Data Architecture. Big Data Cycle Just like every other database related applications, bit data project have its development cycle. Though three Vs (link) for sure plays an important role in deciding the architecture of the Big Data projects. Just like every other project Big Data project also goes to similar phases of the data capturing, transforming, integrating, analyzing and building actionable reporting on the top of  the data. While the process looks almost same but due to the nature of the data the architecture is often totally different. Here are few of the question which everyone should ask before going ahead with Big Data architecture. Questions to Ask How big is your total database? What is your requirement of the reporting in terms of time – real time, semi real time or at frequent interval? How important is the data availability and what is the plan for disaster recovery? What are the plans for network and physical security of the data? What platform will be the driving force behind data and what are different service level agreements for the infrastructure? This are just basic questions but based on your application and business need you should come up with the custom list of the question to ask. As I mentioned earlier this question may look quite simple but the answer will not be simple. When we are talking about Big Data implementation there are many other important aspects which we have to consider when we decide to go for the architecture. Building Blocks of Big Data Architecture It is absolutely impossible to discuss and nail down the most optimal architecture for any Big Data Solution in a single blog post, however, we can discuss the basic building blocks of big data architecture. Here is the image which I have built to explain how the building blocks of the Big Data architecture works. Above image gives good overview of how in Big Data Architecture various components are associated with each other. In Big Data various different data sources are part of the architecture hence extract, transform and integration are one of the most essential layers of the architecture. Most of the data is stored in relational as well as non relational data marts and data warehousing solutions. As per the business need various data are processed as well converted to proper reports and visualizations for end users. Just like software the hardware is almost the most important part of the Big Data Architecture. In the big data architecture hardware infrastructure is extremely important and failure over instances as well as redundant physical infrastructure is usually implemented. NoSQL in Data Management NoSQL is a very famous buzz word and it really means Not Relational SQL or Not Only SQL. This is because in Big Data Architecture the data is in any format. It can be unstructured, relational or in any other format or from any other data source. To bring all the data together relational technology is not enough, hence new tools, architecture and other algorithms are invented which takes care of all the kind of data. This is collectively called NoSQL. Tomorrow Next four days we will answer the Buzz Words – Hadoop. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Big Data – Evolution of Big Data – Day 3 of 21

    - by Pinal Dave
    In yesterday’s blog post we answered what is the Big Data. Today we will understand why and how the evolution of Big Data has happened. Though the answer is very simple, I would like to tell it in the form of a history lesson. Data in Flat File In earlier days data was stored in the flat file and there was no structure in the flat file.  If any data has to be retrieved from the flat file it was a project by itself. There was no possibility of retrieving the data efficiently and data integrity has been just a term discussed without any modeling or structure around. Database residing in the flat file had more issues than we would like to discuss in today’s world. It was more like a nightmare when there was any data processing involved in the application. Though, applications developed at that time were also not that advanced the need of the data was always there and there was always need of proper data management. Edgar F Codd and 12 Rules Edgar Frank Codd was a British computer scientist who, while working for IBM, invented the relational model for database management, the theoretical basis for relational databases. He presented 12 rules for the Relational Database and suddenly the chaotic world of the database seems to see discipline in the rules. Relational Database was a promising land for all the unstructured database users. Relational Database brought into the relationship between data as well improved the performance of the data retrieval. Database world had immediately seen a major transformation and every single vendors and database users suddenly started to adopt the relational database models. Relational Database Management Systems Since Edgar F Codd proposed 12 rules for the RBDMS there were many different vendors who started them to build applications and tools to support the relationship between database. This was indeed a learning curve for many of the developer who had never worked before with the modeling of the database. However, as time passed by pretty much everybody accepted the relationship of the database and started to evolve product which performs its best with the boundaries of the RDBMS concepts. This was the best era for the databases and it gave the world extreme experts as well as some of the best products. The Entity Relationship model was also evolved at the same time. In software engineering, an Entity–relationship model (ER model) is a data model for describing a database in an abstract way. Enormous Data Growth Well, everything was going fine with the RDBMS in the database world. As there were no major challenges the adoption of the RDBMS applications and tools was pretty much universal. There was a race at times to make the developer’s life much easier with the RDBMS management tools. Due to the extreme popularity and easy to use system pretty much every data was stored in the RDBMS system. New age applications were built and social media took the world by the storm. Every organizations was feeling pressure to provide the best experience for their users based the data they had with them. While this was all going on at the same time data was growing pretty much every organization and application. Data Warehousing The enormous data growth now presented a big challenge for the organizations who wanted to build intelligent systems based on the data and provide near real time superior user experience to their customers. Various organizations immediately start building data warehousing solutions where the data was stored and processed. The trend of the business intelligence becomes the need of everyday. Data was received from the transaction system and overnight was processed to build intelligent reports from it. Though this is a great solution it has its own set of challenges. The relational database model and data warehousing concepts are all built with keeping traditional relational database modeling in the mind and it still has many challenges when unstructured data was present. Interesting Challenge Every organization had expertise to manage structured data but the world had already changed to unstructured data. There was intelligence in the videos, photos, SMS, text, social media messages and various other data sources. All of these needed to now bring to a single platform and build a uniform system which does what businesses need. The way we do business has also been changed. There was a time when user only got the features what technology supported, however, now users ask for the feature and technology is built to support the same. The need of the real time intelligence from the fast paced data flow is now becoming a necessity. Large amount (Volume) of difference (Variety) of high speed data (Velocity) is the properties of the data. The traditional database system has limits to resolve the challenges this new kind of the data presents. Hence the need of the Big Data Science. We need innovation in how we handle and manage data. We need creative ways to capture data and present to users. Big Data is Reality! Tomorrow In tomorrow’s blog post we will try to answer discuss Basics of Big Data Architecture. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • New SSIS tool on Codeplex – SSIS Log Analyzer

    I stumbled across a new SSIS tool on Codeplex today, the SSIS Log Analyzer which was only released a few days ago. Whilst it is a beta release and currently only supports 2005 (2008 is promised) it looks quite interesting. It seems to be a fancy log viewer, but with some clever features and a nice looking front-end. I’ve only read the documentation so far, but it has graphs and a debug view that shows your package with the colour animations similar to when debugging in BIDS, and everyone knows, the way the pretty colours and numbers change is the best bit! I’ll quote some of the features for you here and then let you make your own mind up, is it useful in the real world? Option to analyze the logs manually by applying row and column filters over the log data or by using queries to specify more complex criterions. Automated Performance Analysis which provides a quick graphical look on which tasks spent most time during package execution. Rerun (debug) the entire sequence of events which happened during package execution showing the flow of control in graphical form, changes in runtime values for each task like execution duration etc. Support for Auto Analyzers to automatically find out issues and provide suggestions for problems which can be figured out with the help of SSIS logs and/or package. Option to analyze just log file or log and package together. Provides a lightweight environment to have a quick look at the package. Opening it in BIDS takes some time as being an authoring environment it does all sorts of validations resulting in some delay. See http://ssisloganalyzer.codeplex.com/  for more details.

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  • New SSIS tool on Codeplex – SSIS Log Analyzer

    I stumbled across a new SSIS tool on Codeplex today, the SSIS Log Analyzer which was only released a few days ago. Whilst it is a beta release and currently only supports 2005 (2008 is promised) it looks quite interesting. It seems to be a fancy log viewer, but with some clever features and a nice looking front-end. I’ve only read the documentation so far, but it has graphs and a debug view that shows your package with the colour animations similar to when debugging in BIDS, and everyone knows, the way the pretty colours and numbers change is the best bit! I’ll quote some of the features for you here and then let you make your own mind up, is it useful in the real world? Option to analyze the logs manually by applying row and column filters over the log data or by using queries to specify more complex criterions. Automated Performance Analysis which provides a quick graphical look on which tasks spent most time during package execution. Rerun (debug) the entire sequence of events which happened during package execution showing the flow of control in graphical form, changes in runtime values for each task like execution duration etc. Support for Auto Analyzers to automatically find out issues and provide suggestions for problems which can be figured out with the help of SSIS logs and/or package. Option to analyze just log file or log and package together. Provides a lightweight environment to have a quick look at the package. Opening it in BIDS takes some time as being an authoring environment it does all sorts of validations resulting in some delay. See http://ssisloganalyzer.codeplex.com/  for more details.

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  • Working with Temporal Data in SQL Server

    - by Dejan Sarka
    My third Pluralsight course, Working with Temporal Data in SQL Server, is published. I am really proud on the second part of the course, where I discuss optimization of temporal queries. This was a nearly impossible task for decades. First solutions appeared only lately. I present all together six solutions (and one more that is not a solution), and I invented four of them. http://pluralsight.com/training/Courses/TableOfContents/working-with-temporal-data-sql-server

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  • adding custom SSIS transformation to visual studio toolbox fails

    - by ryangaraygay
    Just very recently I encountered an issue in deploying a custom SSIS component assembly which turns out to be a relative "no-brainer" error if only the clues were more straightforward. Basically after deploying the assembly I could not find my component listed in the "SSIS Data Flow Items" tab list.It turns out that the assembly containing the component just had missing or referenced the incorrect assemblies.I have outlined the steps I took that guided me on the right direction on this blog post of mine : adding custom SSIS transformation to visual studio toolbox fails 

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  • SSIS Virtual Class

    - by ejohnson2010
    I recorded a Virtual SSIS Class with the good folks over at SSWUG and the first airing of the class will by May 15th. This is 100% online so you can do it on your own time and from anywhere. The class will run monthly and I will be available for questions through out. You get the following 12 sessions on SSIS, each about an hour. Session 1: The SSIS Basics Session 2: Control Flow Basics Session 3: Data Flow - Sources and Destinations Session 4: Data Flow - Transformations Session 5: Advanced Transformations...(read more)

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  • New Communications Industry Data Model with "Factory Installed" Predictive Analytics using Oracle Da

    - by charlie.berger
    Oracle Introduces Oracle Communications Data Model to Provide Actionable Insight for Communications Service Providers   We've integrated pre-installed analytical methodologies with the new Oracle Communications Data Model to deliver automated, simple, yet powerful predictive analytics solutions for customers.  Churn, sentiment analysis, identifying customer segments - all things that can be anticipated and hence, preconcieved and implemented inside an applications.  Read on for more information! TM Forum Management World, Nice, France - 18 May 2010 News Facts To help communications service providers (CSPs) manage and analyze rapidly growing data volumes cost effectively, Oracle today introduced the Oracle Communications Data Model. With the Oracle Communications Data Model, CSPs can achieve rapid time to value by quickly implementing a standards-based enterprise data warehouse that features communications industry-specific reporting, analytics and data mining. The combination of the Oracle Communications Data Model, Oracle Exadata and the Oracle Business Intelligence (BI) Foundation represents the most comprehensive data warehouse and BI solution for the communications industry. Also announced today, Hong Kong Broadband Network enhanced their data warehouse system, going live on Oracle Communications Data Model in three months. The leading provider increased its subscriber base by 37 percent in six months and reduced customer churn to less than one percent. Product Details Oracle Communications Data Model provides industry-specific schema and embedded analytics that address key areas such as customer management, marketing segmentation, product development and network health. CSPs can efficiently capture and monitor critical data and transform it into actionable information to support development and delivery of next-generation services using: More than 1,300 industry-specific measurements and key performance indicators (KPIs) such as network reliability statistics, provisioning metrics and customer churn propensity. Embedded OLAP cubes for extremely fast dimensional analysis of business information. Embedded data mining models for sophisticated trending and predictive analysis. Support for multiple lines of business, such as cable, mobile, wireline and Internet, which can be easily extended to support future requirements. With Oracle Communications Data Model, CSPs can jump start the implementation of a communications data warehouse in line with communications-industry standards including the TM Forum Information Framework (SID), formerly known as the Shared Information Model. Oracle Communications Data Model is optimized for any Oracle Database 11g platform, including Oracle Exadata, which can improve call data record query performance by 10x or more. Supporting Quotes "Oracle Communications Data Model covers a wide range of business areas that are relevant to modern communications service providers and is a comprehensive solution - with its data model and pre-packaged templates including BI dashboards, KPIs, OLAP cubes and mining models. It helps us save a great deal of time in building and implementing a customized data warehouse and enables us to leverage the advanced analytics quickly and more effectively," said Yasuki Hayashi, executive manager, NTT Comware Corporation. "Data volumes will only continue to grow as communications service providers expand next-generation networks, deploy new services and adopt new business models. They will increasingly need efficient, reliable data warehouses to capture key insights on data such as customer value, network value and churn probability. With the Oracle Communications Data Model, Oracle has demonstrated its commitment to meeting these needs by delivering data warehouse tools designed to fill communications industry-specific needs," said Elisabeth Rainge, program director, Network Software, IDC. "The TM Forum Conformance Mark provides reassurance to customers seeking standards-based, and therefore, cost-effective and flexible solutions. TM Forum is extremely pleased to work with Oracle to certify its Oracle Communications Data Model solution. Upon successful completion, this certification will represent the broadest and most complete implementation of the TM Forum Information Framework to date, with more than 130 aggregate business entities," said Keith Willetts, chairman and chief executive officer, TM Forum. Supporting Resources Oracle Communications Oracle Communications Data Model Data Sheet Oracle Communications Data Model Podcast Oracle Data Warehousing Oracle Communications on YouTube Oracle Communications on Delicious Oracle Communications on Facebook Oracle Communications on Twitter Oracle Communications on LinkedIn Oracle Database on Twitter The Data Warehouse Insider Blog

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  • Big Data – How to become a Data Scientist and Learn Data Science? – Day 19 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned the importance of the analytics in Big Data Story. In this article we will understand how to become a Data Scientist for Big Data Story. Data Scientist is a new buzz word, everyone seems to be wanting to become Data Scientist. Let us go over a few key topics related to Data Scientist in this blog post. First of all we will understand what is a Data Scientist. In the new world of Big Data, I see pretty much everyone wants to become Data Scientist and there are lots of people I have already met who claims that they are Data Scientist. When I ask what is their role, I have got a wide variety of answers. What is Data Scientist? Data scientists are the experts who understand various aspects of the business and know how to strategies data to achieve the business goals. They should have a solid foundation of various data algorithms, modeling and statistics methodology. What do Data Scientists do? Data scientists understand the data very well. They just go beyond the regular data algorithms and builds interesting trends from available data. They innovate and resurrect the entire new meaning from the existing data. They are artists in disguise of computer analyst. They look at the data traditionally as well as explore various new ways to look at the data. Data Scientists do not wait to build their solutions from existing data. They think creatively, they think before the data has entered into the system. Data Scientists are visionary experts who understands the business needs and plan ahead of the time, this tremendously help to build solutions at rapid speed. Besides being data expert, the major quality of Data Scientists is “curiosity”. They always wonder about what more they can get from their existing data and how to get maximum out of future incoming data. Data Scientists do wonders with the data, which goes beyond the job descriptions of Data Analysist or Business Analysist. Skills Required for Data Scientists Here are few of the skills a Data Scientist must have. Expert level skills with statistical tools like SAS, Excel, R etc. Understanding Mathematical Models Hands-on with Visualization Tools like Tableau, PowerPivots, D3. j’s etc. Analytical skills to understand business needs Communication skills On the technology front any Data Scientists should know underlying technologies like (Hadoop, Cloudera) as well as their entire ecosystem (programming language, analysis and visualization tools etc.) . Remember that for becoming a successful Data Scientist one require have par excellent skills, just having a degree in a relevant education field will not suffice. Final Note Data Scientists is indeed very exciting job profile. As per research there are not enough Data Scientists in the world to handle the current data explosion. In near future Data is going to expand exponentially, and the need of the Data Scientists will increase along with it. It is indeed the job one should focus if you like data and science of statistics. Courtesy: emc Tomorrow In tomorrow’s blog post we will discuss about various Big Data Learning resources. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • SSIS Lookup component tuning tips

    - by jamiet
    Yesterday evening I attended a London meeting of the UK SQL Server User Group at Microsoft’s offices in London Victoria. As usual it was both a fun and informative evening and in particular there seemed to be a few questions arising about tuning the SSIS Lookup component; I rattled off some comments and figured it would be prudent to drop some of them into a dedicated blog post, hence the one you are reading right now. Scene setting A popular pattern in SSIS is to use a Lookup component to determine whether a record in the pipeline already exists in the intended destination table or not and I cover this pattern in my 2006 blog post Checking if a row exists and if it does, has it changed? (note to self: must rewrite that blog post for SSIS2008). Fundamentally the SSIS lookup component (when using FullCache option) sucks some data out of a database and holds it in memory so that it can be compared to data in the pipeline. One of the big benefits of using SSIS dataflows is that they process data one buffer at a time; that means that not all of the data from your source exists in the dataflow at the same time and is why a SSIS dataflow can process data volumes that far exceed the available memory. However, that only applies to data in the pipeline; for reasons that are hopefully obvious ALL of the data in the lookup set must exist in the memory cache for the duration of the dataflow’s execution which means that any memory used by the lookup cache will not be available to be used as a pipeline buffer. Moreover, there’s an obvious correlation between the amount of data in the lookup cache and the time it takes to charge that cache; the more data you have then the longer it will take to charge and the longer you have to wait until the dataflow actually starts to do anything. For these reasons your goal is simple: ensure that the lookup cache contains as little data as possible. General tips Here is a simple tick list you can follow in order to tune your lookups: Use a SQL statement to charge your cache, don’t just pick a table from the dropdown list made available to you. (Read why in SELECT *... or select from a dropdown in an OLE DB Source component?) Only pick the columns that you need, ignore everything else Make the database columns that your cache is populated from as narrow as possible. If a column is defined as VARCHAR(20) then SSIS will allocate 20 bytes for every value in that column – that is a big waste if the actual values are significantly less than 20 characters in length. Do you need DT_WSTR typed columns or will DT_STR suffice? DT_WSTR uses twice the amount of space to hold values that can be stored using a DT_STR so if you can use DT_STR, consider doing so. Same principle goes for the numerical datatypes DT_I2/DT_I4/DT_I8. Only populate the cache with data that you KNOW you will need. In other words, think about your WHERE clause! Thinking outside the box It is tempting to build a large monolithic dataflow that does many things, one of which is a Lookup. Often though you can make better use of your available resources by, well, mixing things up a little and here are a few ideas to get your creative juices flowing: There is no rule that says everything has to happen in a single dataflow. If you have some particularly resource intensive lookups then consider putting that lookup into a dataflow all of its own and using raw files to pass the pipeline data in and out of that dataflow. Know your data. If you think, for example, that the majority of your incoming rows will match with only a small subset of your lookup data then consider chaining multiple lookup components together; the first would use a FullCache containing that data subset and the remaining data that doesn’t find a match could be passed to a second lookup that perhaps uses a NoCache lookup thus negating the need to pull all of that least-used lookup data into memory. Do you need to process all of your incoming data all at once? If you can process different partitions of your data separately then you can partition your lookup cache as well. For example, if you are using a lookup to convert a location into a [LocationId] then why not process your data one region at a time? This will mean your lookup cache only has to contain data for the location that you are currently processing and with the ability of the Lookup in SSIS2008 and beyond to charge the cache using a dynamically built SQL statement you’ll be able to achieve it using the same dataflow and simply loop over it using a ForEach loop. Taking the previous data partitioning idea further … a dataflow can contain more than one data path so why not split your data using a conditional split component and, again, charge your lookup caches with only the data that they need for that partition. Lookups have two uses: to (1) find a matching row from the lookup set and (2) put attributes from that matching row into the pipeline. Ask yourself, do you need to do these two things at the same time? After all once you have the key column(s) from your lookup set then you can use that key to get the rest of attributes further downstream, perhaps even in another dataflow. Are you using the same lookup data set multiple times? If so, consider the file caching option in SSIS 2008 and beyond. Above all, experiment and be creative with different combinations. You may be surprised at what works. Final  thoughts If you want to know more about how the Lookup component differs in SSIS2008 from SSIS2005 then I have a dedicated blog post about that at Lookup component gets a makeover. I am on a mini-crusade at the moment to get a BULK MERGE feature into the database engine, the thinking being that if the database engine can quickly merge massive amounts of data in a similar manner to how it can insert massive amounts using BULK INSERT then that’s a lot of work that wouldn’t have to be done in the SSIS pipeline. If you think that is a good idea then go and vote for BULK MERGE on Connect. If you have any other tips to share then please stick them in the comments. Hope this helps! @Jamiet Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

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  • Unstructured Data - The future of Data Administration

    Some have claimed that there is a problem with the way data is currently managed using the relational paradigm do to the rise of unstructured data in modern business. PCMag.com defines unstructured data as data that does not reside in a fixed location. They further explain that unstructured data refers to data in a free text form that is not bound to any specific structure. With the rise of unstructured data in the form of emails, spread sheets, images and documents the critics have a right to argue that the relational paradigm is not as effective as the object oriented data paradigm in managing this type of data. The relational paradigm relies heavily on structure and relationships in and between items of data. This type of paradigm works best in a relation database management system like Microsoft SQL, MySQL, and Oracle because data is forced to conform to a structure in the form of tables and relations can be derived from the existence of one or more tables. These critics also claim that database administrators have not kept up with reality because their primary focus in regards to data administration deals with structured data and the relational paradigm. The relational paradigm was developed in the 1970’s as a way to improve data management when compared to standard flat files. Little has changed since then, and modern database administrators need to know more than just how to handle structured data. That is why critics claim that today’s data professionals do not have the proper skills in order to store and maintain data for modern systems when compared to the skills of system designers, programmers , software engineers, and data designers  due to the industry trend of object oriented design and development. I think that they are wrong. I do not disagree that the industry is moving toward an object oriented approach to development with the potential to use more of an object oriented approach to data.   However, I think that it is business itself that is limiting database administrators from changing how data is stored because of the potential costs, and impact that might occur by altering any part of stored data. Furthermore, database administrators like all technology workers constantly are trying to improve their technical skills in order to excel in their job, so I think that accusing data professional is not just when the root cause of the lack of innovation is controlled by business, and it is business that will suffer for their inability to keep up with technology. One way for database professionals to better prepare for the future of database management is start working with data in the form of objects and so that they can extract data from the objects so that the stored information within objects can be used in relation to the data stored in a using the relational paradigm. Furthermore, I think the use of pattern matching will increase with the increased use of unstructured data because object can be selected, filtered and altered based on the existence of a pattern found within an object.

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  • Developing an analytics's system processing large amounts of data - where to start

    - by Ryan
    Imagine you're writing some sort of Web Analytics system - you're recording raw page hits along with some extra things like tagging cookies etc and then producing stats such as Which pages got most traffic over a time period Which referers sent most traffic Goals completed (goal being a view of a particular page) And more advanced things like which referers sent the most number of vistors who later hit a goal. The naieve way of approaching this would be to throw it in a relational database and run queries over it - but that won't scale. You could pre-calculate everything (have a queue of incoming 'hits' and use to update report tables) - but what if you later change a goal - how could you efficiently re-calculate just the data that would be effected. Obviously this has been done before ;) so any tips on where to start, methods & examples, architecture, technologies etc.

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  • Big Data – Beginning Big Data – Day 1 of 21

    - by Pinal Dave
    What is Big Data? I want to learn Big Data. I have no clue where and how to start learning about it. Does Big Data really means data is big? What are the tools and software I need to know to learn Big Data? I often receive questions which I mentioned above. They are good questions and honestly when we search online, it is hard to find authoritative and authentic answers. I have been working with Big Data and NoSQL for a while and I have decided that I will attempt to discuss this subject over here in the blog. In the next 21 days we will understand what is so big about Big Data. Big Data – Big Thing! Big Data is becoming one of the most talked about technology trends nowadays. The real challenge with the big organization is to get maximum out of the data already available and predict what kind of data to collect in the future. How to take the existing data and make it meaningful that it provides us accurate insight in the past data is one of the key discussion points in many of the executive meetings in organizations. With the explosion of the data the challenge has gone to the next level and now a Big Data is becoming the reality in many organizations. Big Data – A Rubik’s Cube I like to compare big data with the Rubik’s cube. I believe they have many similarities. Just like a Rubik’s cube it has many different solutions. Let us visualize a Rubik’s cube solving challenge where there are many experts participating. If you take five Rubik’s cube and mix up the same way and give it to five different expert to solve it. It is quite possible that all the five people will solve the Rubik’s cube in fractions of the seconds but if you pay attention to the same closely, you will notice that even though the final outcome is the same, the route taken to solve the Rubik’s cube is not the same. Every expert will start at a different place and will try to resolve it with different methods. Some will solve one color first and others will solve another color first. Even though they follow the same kind of algorithm to solve the puzzle they will start and end at a different place and their moves will be different at many occasions. It is  nearly impossible to have a exact same route taken by two experts. Big Market and Multiple Solutions Big Data is exactly like a Rubik’s cube – even though the goal of every organization and expert is same to get maximum out of the data, the route and the starting point are different for each organization and expert. As organizations are evaluating and architecting big data solutions they are also learning the ways and opportunities which are related to Big Data. There is not a single solution to big data as well there is not a single vendor which can claim to know all about Big Data. Honestly, Big Data is too big a concept and there are many players – different architectures, different vendors and different technology. What is Next? In this 31 days series we will be exploring many essential topics related to big data. I do not claim that you will be master of the subject after 31 days but I claim that I will be covering following topics in easy to understand language. Architecture of Big Data Big Data a Management and Implementation Different Technologies – Hadoop, Mapreduce Real World Conversations Best Practices Tomorrow In tomorrow’s blog post we will try to answer one of the very essential questions – What is Big Data? Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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