Analytics

Typical Issues:

• High latency for analytics on terabyte to petabyte data sizes
• Business analysts are forced to sample data risking undiscovered patterns, limited insight and missed events
• Advanced analysis techniques such as time-series, clustering, graph, market basket, etc., are not available to analysts in a timely manner or at all

Causes:

• Parallel processing hurdle is too high for most organizations
  • Developers must have specialized skills to program for parallelism
  • Native Hadoop MapReduce is not business-analyst friendly
• Advanced analytic toolsets rely on a high latency data pipeline
  • Analysts must prepare and move data out of the data warehouse for advanced analytics
• Advanced analytics stress the limitations of standard SQL alone
  • Multiple self-joins slow query performance
  • Minor application/analytics modifications require significant SQL code changes

The Solution:

• SQL-MapReduce
  • Integrates SQL with MapReduce parallel processing framework
  • Power of MapReduce with familiarity of SQL
  • Automatically parallelizes analytic applications without requiring design for parallelization
• In-Database Analytics
  • 100% of Analytic computations run in-database, close to the data, eliminating massive data movement
• Aster Data Analytic Foundation
  • Suite of SQL-MapReduce business analyst-ready functions that provide high performance analysis for advanced analytics like time-series, clustering, graph, market basket analysis
  • Pre-defined functions are modifiable by the business analyst simply through parameter changes - no additional time-intensive coding required