High Performance Data Management for Advanced Analytics

Webinar Abstract
Big data is putting a big hurt on traditional data warehousing. Data volumes are rising fast, and it’s getting harder to keep up with the variety and velocity of data using standard procedures for extraction, transformation, and loading. In addition, to gain business value from this torrent of data, a growing number of users want to perform advanced analytics that go beyond what most business intelligence and online analytical processing (OLAP) systems can support. Complex queries for advanced analytics need continuous access to large volumes of detailed, and in some cases near real-time data. This can put a huge strain on standard data warehousing procedures and overall data management.

To be competitive in this data-driven age, organizations must find solutions. High-performance computing technologies and methods are expanding opportunities for organizations to meet requirements for advanced analytics, enabling them to discover insights that would be beyond the grasp of BI and OLAP. High-performance computing leverages powerful hardware and distributed networking technology such as multi-core processing, massively parallel processing, and very large memory to increase power and scalability. The impact on analytics is that organizations can now affordably employ in-memory and in-database processing, grid computing, and data federation to accomplish far more than they could with traditional systems.

In this Webinar, TDWI will examine seven key technology trends in high performance computing that are changing the landscape for advanced analytics and enabling organizations to solve pressing data management challenges they are facing with traditional ETL and data warehousing systems.

What You Will Learn:

  • Seven steps you can take to leverage high-performance computing for advanced analytics
  • How in-database processing and ELT can increase the speed and manageability of analytics
  • Why in-memory processing is important for complex analytical query performance and how you can avoid potential problems with this approach
  • How workload management can help you gain the benefits of grid and parallel computing for advanced analytics

David Stodder

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