jagsr123

4 ms·

1.
by jagsr123 10y ago
Good questions. One answer - Speed in analytics when working with large data sets matters. A lot. Think about this - several vendors seem to be claiming support for interactive analytics. i.e. i can ask an adhoc question on large volume of
2.
by jagsr123 10y ago
Yes, it is a hash join.
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by jagsr123 10y ago
From what I know, this test originally written by Databricks (expanded here) is meant to tease out the optimizations in the Tungsten engine. Of course, a distributed query that is dominated by shuffle costs will produce a very different res
4.
by jagsr123 10y ago
Just to be clear, we do support Snappydata as a library (unfortunately, the docs don't make this clear) that can work with your distribution (upto 1.6 today). Our releases will support the latest Spark version in a staggered manner. Ev
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by jagsr123 10y ago
Structured streaming really makes spark much more competitive for streaming use cases by allowing streams to be seen as DataFrames and hence make it super simple to run continuous queries using SQL on streams, but, often you also need to wo
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by jagsr123 11y ago
Yes. Hopefully soon.
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by jagsr123 11y ago
@superkk Ironically, we debated this extensively and concluded the reverse (primarily because we believe the Spark API is succinct and integration with the eco-system will be easy) - much more like spark than Gemfire. In fact, the API is pr
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by jagsr123 11y ago
Metabase looks very interesting. We will go through it in more detail. Can you point to any doc on what it takes to create a connector? Note that Snappydata has two straightforward entry points - SQL (e.g. JDBC connector) or using the Spar