Scaling

Typical data warehouse scaling issues:

  • Too expensive to scale data warehouse
  • Too much planning and administration required to scale

Causes:

  • Inflexible systems
    • Advanced planning required to move data correctly
    • Complexity: repartition, re-indexing, constraints, etc.
  • “Super-size” upgrades
    • Existing systems limited by hardware constraints – no salvage value
    • Proprietary and expensive hardware pre-configured in cabinets and “rolled in” (at huge cost to you)
    • Huge costs in people resources/time to migrate data over, set up the new configurations, and hope everything works
    • Repeat as data volumes grow
  • Downtime required
    • Upgrades are complex and require hours, if not days to complete
    • Extra data loading time required to compensate for downtime
  • Error prone upgrades
    • Complexity leads to user error or system glitches
    • Improper load balancing and poor performance typical as kinks are worked out

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 have done an analysis of what is currently available in the market concerning modern large-scale data analytics solutions, and Aster Data had the most complete product that met our requirements for rich analytics, scale, speed and overall cost of ownership.

Telefonica I+D
Richard Benjamins, Director of User Modeling