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:

• Solving the network bottleneck in MPP databases
• Online Precision Scaling