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An overview of what makes an Analytics Platform and how Aster Data massively parallel DB enables analytic processing
Smart Data Collective, January 26, 2011 by Ryan Garrett
When we kicked off Aster Data back in 2005, we envisioned building a product that would advance the state of the art in data management in two areas; (1) size and diversity of data and (2) depth of insight/analytics. My co-founders and I quickly realized that building just another database wouldn't cut it. With yet-another-database, even if we enabled companies to more cost-effectively manage large data sizes, it was not going to be enough given the explosion in diverse data types and the massive need to process all of it. So we set out to build a new platform that would solve these challenges - what's now commonly known as the 'Big Data' challenge.
Fast forward to 2008 when Aster Data led the way in putting massive parallel processing inside a MPP database, using MapReduce, to advance how you process massive amounts of diverse data. While this was fully aligned with our vision for managing hoards of diverse data and allowing deep data processing in a single platform, most thought it was intriguing but couldn't quite see the light in terms of where the future was going. At one point, we thought of naming our product XAP - "extreme analytic platform" or "extreme analytic processing" as that's what it was designed to do from day one. However, we thought better of it since we thought we would have to educate people too much on what an "analytic platform" was and how it was different from a traditional DBMS for data warehousing. Since we also were serving the data architects in organizations as well as the front-line business that demands better, faster analytics, we needed to use terminology that resonated with both.
By late 2010, the term "analytic platform" started to take shape. The definition of it fit exactly with what Aster Data has built. And now, traditional DW appliances are claiming to be analytic platforms. Even Netezza is taking the same box they had before and calling it "An Appliance for Deep Business Analytics," and pure columnar MPP DBMS's like Vertica and ParAccel overnight went from being 'the world's fastest database' to ALL claiming to be an analytic(s) platform. This is a typical marketing trajectory if you now see where the future lies in big data management. The market as a whole is gravitating to accept that if you truly want to manage big, diverse data, you ultimately want to analyze all of it, and for that you're really in need of a big data analytic platform - not just a big data store.
I predict this year will be the year where the analytic platform - which we at Aster Data started to talk about and deliver in 2008 - will now be a distinct and unique category: distinct from an enterprise data warehouse (EDW); distinct from traditional DBMSs; distinct from even some pure MPP DBMSs; and distinct from even Hadoop.
An analytic platform, put simply, must have the following:
1. Native in-database processing engine ...
2. Native support for MapReduce. ...
3. Tight integration with SQL. ...
4. Enterprise feature support for in-database applications ...
5. Making in-database application development EASY and cost-effective. ...
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