Data Growth

Typical Issues:

  • Overwhelming volume forces you to sample data
  • Lack of data warehouse 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: New MapReduce Whitepaper
Webcast: Bringing Big Data Analytics to the Enterprise - 11/12, with Merv Adrian
Webinar: Service Oriented 'Analytics' - 11/19, with James Kobelius
We're excited about In-Database MapReduce and the promise it offers of scalable execution of advanced statistics without having to move data to a separate statistics platform.

Invite Media
Scott Becker, CTO