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  • SSIS Reporting Pack update

    - by jamiet
    Its been a while since I last posted anything in regard to SSIS Reporting Pack, the most recent release being on 27th May 2012, so here is a short update. There is still lots of work to do on SSIS Reporting Pack; lots more features to add, lots of performance work to be done, and a few bug fixes too. I have also been (fairly) hard at work on a framework to be used in conjunction with SSIS 2012 that I refer to as the Restart Framework (currently residing at http://ssisrestartframework.codeplex.com/). There is still much work to be done on the Restart Framework (not least some useful documentation on how to use it) which is why I haven’t mentioned it publicly before now although I am actively checking in changes. One thing I am considering is amalgamating the two projects into one; this would mean I could build a suite of reports that both work against the SSIS Catalog (what you currently know as “SSIS Reporting Pack”) and also against this Restart Framework thing. No decision has been made as yet though. There have been a number of bug reports and feature suggestions for SSIS Reporting Pack added to the Issue Tracker. Thank you to everyone that has submitted something, rest assured I am not going to ignore them forever; my time is at a premium right now unfortunately due to … well … life… so working on these items isn’t near the top of my priority list. Lastly, I am actively using SSIS Reporting Pack in a production environment right now and I’m happy to report that it is proving to be very useful. One of the reports that I have put a lot of time into is execution executable duration.rdl and its proving very adept at easily identifying bottlenecks in our SSIS 2012 executions: The report allows you to browse through the hierarchy of executables in each execution and each bar represents the duration of each executable in relation to all the other executables; longer bars being a good indication of where problems might lie. The colour of the bar indicates whether it was successful or not (green=success). Hovering over a bar brings up a tooltip showing more information about that executable. Clicking on a bar allows you to compare this particular instance of the executable against other executions. Please do let me know if you are using SSIS Reporting Pack. I would like to hear any anecdotes you might have, good or bad. @Jamiet

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  • The current state of a MERGE Destination for SSIS

    - by jamiet
    Hugo Tap asked me on Twitter earlier today whether or not there existed a SSIS Dataflow Destination component that enabled one to MERGE data into a table rather than INSERT it. Its a common request so I thought it might be useful to summarise the current state of play as regards a MERGE destination for SSIS. Firstly, there is no MERGE destination component in the box; that is, when you install SSIS no MERGE Destination will be available. That being said the SSIS team have made available a MERGE destination component via Codeplex which you can get from http://sqlsrvintegrationsrv.codeplex.com/releases/view/19048. I have never used it so cannot vouch for its usefulness although judging by some of the reviews you might not want to set your expectations too high. Your mileage may vary.   In the past it has occurred to me that a built-in way to provide MERGE from the SSIS pipeline would be highly valuable. I assume that this would have to be provided by the database into which you were merging hence in March 2010 I submitted the following two requests to Connect: BULK MERGE (111 votes at the time of writing) [SSIS] BULK MERGE Destination (15 votes) If you think these would be useful feel free to vote them up and add a comment. Lastly, this one is nothing to do with SSIS but if you want to perform a minimally logged MERGE using T-SQL Sunil Agarwal has explained how at Minimal logging and MERGE statement. @Jamiet

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  • Upgrade SSIS 2005 Packages to SSIS 2008

    There are several enhancements in SSIS 2008 such as enhanced lookup transformation, the development environment for Script Task and Script Component changing from VSA to VSTA, etc. If you intend to upgrade your SSIS 2005 packages to SSIS 2008 ... [Read Full Article]

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  • SSIS Denali as part of “Enterprise Information Management”

    - by jorg
    When watching the SQL PASS session “What’s Coming Next in SSIS?” of Steve Swartz, the Group Program Manager for the SSIS team, an interesting question came up: Why is SSIS thought of to be BI, when we use it so frequently for other sorts of data problems? The answer of Steve was that he breaks the world of data work into three parts: Process of inputs BI   Enterprise Information Management All the work you have to do when you have a lot of data to make it useful and clean and get it to the right place. This covers master data management, data quality work, data integration and lineage analysis to keep track of where the data came from. All of these are part of Enterprise Information Management. Next, Steve told Microsoft is developing SSIS as part of a large push in all of these areas in the next release of SQL. So SSIS will be, next to a BI tool, part of Enterprise Information Management in the next release of SQL Server. I'm interested in the different ways people use SSIS, I've basically used it for ETL, data migrations and processing inputs. In which ways did you use SSIS?

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  • Free Webinar - Using Enterprise Data Integration Dashboards

    - by andyleonard
    Join Kent Bradshaw and me as we present Using Enterprise Data Integration Dashboards Tuesday 11 Dec 2012 at 10:00 AM ET! If data is the life of the modern organization, data integration is the heart of an enterprise. Data circulation is vital. Data integration dashboards provide enterprise ETL (Extract, Transform, and Load) teams near-real-time status supported with historical performance analysis. Join Linchpins Kent Bradshaw and Andy Leonard as they demonstrate and discuss the benefits of data...(read more)

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  • Join the Authors of SSIS Design Patterns at the PASS Summit 2012!

    - by andyleonard
    My fellow authors and I will be presenting a day-long pre-conference session titled SSIS Design Patterns at the PASS Summit 2012 in Seattle Monday 5 Nov 2012! Register to learn patterns for: Package execution Package logging Loading flat file sources Loading XML sources Loading the cloud Dynamic package generation SSIS Frameworks Data warehouse ETL Data flow performance   Presenting this session: Matt Masson Tim Mitchell Jessica Moss Michelle Ufford Andy Leonard I hope to see you in Seattle!...(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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  • 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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  • Inequality joins, Asynchronous transformations and Lookups : SSIS

    - by jamiet
    It is pretty much accepted by SQL Server Integration Services (SSIS) developers that synchronous transformations are generally quicker than asynchronous transformations (for a description of synchronous and asynchronous transformations go read Asynchronous and synchronous data flow components). Notice I said “generally” and not “always”; there are circumstances where using asynchronous transformations can be beneficial and in this blog post I’ll demonstrate such a scenario, one that is pretty common when building data warehouses. Imagine I have a [Customer] dimension table that manages information about all of my customers as a slowly-changing dimension. If that is a type 2 slowly changing dimension then you will likely have multiple rows per customer in that table. Furthermore you might also have datetime fields that indicate the effective time period of each member record. Here is such a table that contains data for four dimension members {Terry, Max, Henry, Horace}: Notice that we have multiple records per customer and that the [SCDStartDate] of a record is equivalent to the [SCDEndDate] of the record that preceded it (if there was one). (Note that I am on record as saying I am not a fan of this technique of storing an [SCDEndDate] but for the purposes of clarity I have included it here.) Anyway, the idea here is that we will have some incoming data containing [CustomerName] & [EffectiveDate] and we need to use those values to lookup [Customer].[CustomerId]. The logic will be: Lookup a [CustomerId] WHERE [CustomerName]=[CustomerName] AND [SCDStartDate] <= [EffectiveDate] AND [EffectiveDate] <= [SCDEndDate] The conventional approach to this would be to use a full cached lookup but that isn’t an option here because we are using inequality conditions. The obvious next step then is to use a non-cached lookup which enables us to change the SQL statement to use inequality operators: Let’s take a look at the dataflow: Notice these are all synchronous components. This approach works just fine however it does have the limitation that it has to issue a SQL statement against your lookup set for every row thus we can expect the execution time of our dataflow to increase linearly in line with the number of rows in our dataflow; that’s not good. OK, that’s the obvious method. Let’s now look at a different way of achieving this using an asynchronous Merge Join transform coupled with a Conditional Split. I’ve shown it post-execution so that I can include the row counts which help to illustrate what is going on here: Notice that there are more rows output from our Merge Join component than on the input. That is because we are joining on [CustomerName] and, as we know, we have multiple records per [CustomerName] in our lookup set. Notice also that there are two asynchronous components in here (the Sort and the Merge Join). I have embedded a video below that compares the execution times for each of these two methods. The video is just over 8minutes long. View on Vimeo  For those that can’t be bothered watching the video I’ll tell you the results here. The dataflow that used the Lookup transform took 36 seconds whereas the dataflow that used the Merge Join took less than two seconds. An illustration in case it is needed: Pretty conclusive proof that in some scenarios it may be quicker to use an asynchronous component than a synchronous one. Your mileage may of course vary. The scenario outlined here is analogous to performance tuning procedural SQL that uses cursors. It is common to eliminate cursors by converting them to set-based operations and that is effectively what we have done here. Our non-cached lookup is performing a discrete operation for every single row of data, exactly like a cursor does. By eliminating this cursor-in-disguise we have dramatically sped up our dataflow. I hope all of that proves useful. You can download the package that I demonstrated in the video from my SkyDrive at http://cid-550f681dad532637.skydrive.live.com/self.aspx/Public/BlogShare/20100514/20100514%20Lookups%20and%20Merge%20Joins.zip Comments are welcome as always. @Jamiet Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

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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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