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 analytic techniques such as pattern, time-series, clustering, graph, and market basket analysis are not available to analysts in a timely manner or at all
Causes:
- Parallel programming is too complex 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
- Embedded Analytics
- 100% of analytic computations run in-database, close to the data, eliminating massive data movement
- Aster Data Analytic Foundation
- Suite of pre-built SQL-MapReduce functions that provide high performance analysis for advanced analytics including pattern,time-series, clustering, graph, and market basket analysis
- Pre-defined functions are modifiable by the business analyst simply through parameter changes - no additional time-intensive coding required
- Hybrid Row/Column Database
- Unified SQL-MapReduce computation layer can access data optimized for performance through either a row store, column store, or a combination of both
- Both row and column stores are first class citizens in the database architecture, sharing a unified set of data services for ease of management and seamless data access
- Data storage recommendations provided based on workload to ease modeling effort
Video
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Whitepaper: A New Approach for Large-Scale Data Management and Data Analysis |
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Whitepaper: Deriving Deep Insights from Large Datasets |
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Forrester Report: In-Database Analytics: The Heart of the Predictive Enterprise |
 Now that all our data is in one place, we can understand customer interactions across our entire [retail/ online/e-reader] ecosystem. (MapReduce helps researchers) see trends more quickly than possible in systems only using massively parallel processing.  Marc Parrish, Vice President, Direct Marketing |
With Aster ... our data load time has decreased by over 95 percent, and our most important queries complete in seconds or less. Tim Schigel, CTO |
