Aster’s Big Data Architecture


As I mentioned in my last entry, the week before last I headed out to the TDWI World Conference in San Diego.  Besides talking about Dell’s new BI practice, I was there to represent our data analytics partners, Aster Data and Greenplum.  Both vendors also had booths of their own and I was able to grab some time with Jeff Zeisler, director of pre-sales engineers at Aster Data, to get an overview of their architecture.  Here’s what Jeff had to say:

Some of the ground Jeff covers:

  • Aster is a MPP (massively-parallel processing) data warehouse solution.  It runs on a cluster of commodity hardware that execute SQL queries in parallel.
  • The 3 layers to the architecture:
    • Queen tier – central location users use to submit queries. It figures out how to split up the query and send it to the next tier.
    • Worker tier – where most of the servers are located, where data is stored (locally on the servers) and where all the heavy lifting for processing occurs.  The map reduce framework is built into this tier and sits right next to the SQL execution engine.
    • Loader and exporter tier:  a separate tier of machines that can be used to load new data into the system for  bulk loading.
  • How it works: Query gets broken up across all the machines, they each execute some portion of the query and the result are brought back together at the Queen and returned to the user.
  • New cool things coming up in the next 6 months.

Extra:

Pau for now…

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4 Responses to Aster’s Big Data Architecture

  1. Awesome! Really liked the layered solution and how they work together. Very interesting solution indeed.

    George.

  2. [...] An Overview: Aster’s Big Data Architecture [...]

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