Query Performance

Typical business intelligence and data warehouse issues:

  • Queries take too long to finish or don't finish at all
  • Performance degrades as data increases and queries increase


  • Concurrency
    • Too many users hitting the system at once
    • Queries being run are too complex
  • Data volume chokes system
    • Rich queries require computations that a single node vertically-scaled database cannot handle
    • Overloaded system causes errors to return an error to the user, or hours/days of waiting
    • Can store the data but can't analyze – data is outpacing the fixed amount of CPU, memory, and network resources
  • Network becomes bottleneck
    • Network interconnect choked due to the sheer amount of data
    • Queries run inefficiently, requiring multiple passes over data or materialized views


  • Parallel processing architecture
    • Support high user concurrency
    • Scale out using inexpensive commodity nodes
  • In-Database Analytics
    • In-Database MapReduce
    • Expressive analytic flexibility
    • High-performance analytic processing where the data resides
    • Reusability of SQL-MapReduce components
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
One of the reasons we chose Aster Data is because of their deep MapReduce implementation that speeds data processing and helps give our customers even faster performance and more granular information so they can drive more traffic and transactions through organic search.

Richard Zwicky, Founder and President
Aster Data provides powerful analytics capabilities that cut down our end-to-end fraud analysis cycle time from 7 days to 15 minutes, and queries that took 90 minutes now execute in 90 seconds.

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