Data Growth

Typical Issues:

  • Overwhelming volume forces you to sample data for analysis
  • Lack of capacity forces unwanted data expiration/archiving
  • Slow queries force data aggregates or summaries

Causes:

  • Network bottleneck
    • Queries beyond simple scans and look-ups require data shuffling between nodes
    • Large data volumes flowing between nodes
  • Upgrade and Scaling Costs
    • Too expensive to store and analyze additional data
    • Scaling takes too long. Complex systems require long-range planning
    • Costly “super-size” upgrades since new hardware isn't compatible with old, inflexible system
    • Time involved with migrating systems data to new system
  • Lack of sophisticated analytical tools
    • Complex business analysis demands
    • SQL pushed beyond limits for large data sets

Remedies:

Top Picks
Whitepaper: A New Approach for Large-Scale Data Management and Data Analysis
Whitepaper: Deriving Deep Insights from Large Datasets
Forrester Report: In-Database Analytics: The Heart of the Predictive Enterprise