APS Web Series Shares Insight with Real World Performance Comparisons

The confluence of databases with traditional symmetric multiprocessing (SMP) architecture, the growth of big data, and businesses wanting to use that data to make better business decisions has reached a critical point.

IT administrators simply can’t keep up with the demand for delivering big data insights, satisfying end-users who want speed and mobility, and data that’s expected to grow tenfold over the next five years without major investments in hardware, tuning, support, and maintenance.

If you’ve been looking for solutions that can provide a scalable system that handles tens of TBs of data up to PBs and performance while also helping you find big data insight, Microsoft Analytics Platform System (APS) may be the solution that can help your organization transition to a modern data warehouse. 

Register now for the upcoming webinar series to learn more about this game changing solution and how it can help unify non-relational data from HDInsight, Microsoft’s 100-percent Apache distribution of Hadoop based on the Hortonworks Data Platform, with relational data from Microsoft SQL Server Parallel Data Warehouse (PDW), a massively parallel processing (MPP) relational data warehouse, into a single integrated appliance.


If you’ve been considering a migration from SQL Server, sign up for this informative and engaging web series that’ll show how to take advantage of APS to achieve the following:

  • Identify workloads that are suited for APS/PDW
  • Improved MPP performance with SQL Server PDW
  • Seamless Hadoop integration with relational data using TransactSQL (T-SQL) in PolyBase
  • Integrated Hadoop performance through HDInsight (optional)

Register Today

Join us for the webinar series for an overview of APS and see real world performance comparisons for those considering a migration from SQL Server.

The first web series class Analytics Platform System overview starts at 11 a.m. on Wednesday, July 15.

Show me the entire schedule of classes.

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