1.1B taxi rides benchmarked on distributed GPU-powered MapD

1.1B records = 500GB of raw CSV data. This fits into RAM quite easily on a machine like the P2.8xlarge, especially when compression is used (like MapD uses).<p>I&#x27;d like to see how well this performs on a dataset that doesn&#x27;t fit in the RAM.

Is there a bridge for using MapD with Spark interface or somehow combining them? This can be interesting for the clusters with a lot of GPUs and a lot of data to do data manipulations.

Found elsewhere on the internet: &#x27;On a system with eight Tesla K80s, which might cost somewhere between $60,000 to $70,000, the license for the MapD stack would be “a small multiple” of this hardware cost.&#x27;<p>I guess I&#x27;m not playing with this anytime soon.

Amazing to see the improvements that MapD has made over the past few years. I have been following them for a long time, and was excited to catch wind of 3.0 this morning. Then I get on here to see someone already benchmarking and working with it.

the benchmark page [1] mark did a good summary of how various technologies compare.<p>[1] <a href="http:&#x2F;&#x2F;tech.marksblogg.com&#x2F;benchmarks.html" rel="nofollow">http:&#x2F;&#x2F;tech.marksblogg.com&#x2F;benchmarks.html</a>

Price comparison between Amazon Redshift, Google BigQuery, ElasticSearch and SlicingDice using the same dataset:<p><a href="https:&#x2F;&#x2F;blog.slicingdice.com&#x2F;slicingdice-pricing-model-and-competitors-comparison-31f1c9f0f076" rel="nofollow">https:&#x2F;&#x2F;blog.slicingdice.com&#x2F;slicingdice-pricing-model-and-c...</a>