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Shark: All content tagged as Shark in NoSQL databases and polyglot persistence

SQL on Hadoop: An overview of frameworks and their applicability

An overview of the 3 SQL-on-Hadoop execution models — batch (10s of minutes and up), interactive (up to minutes), operational (sub-second), their applicability in the field of applications, and the main characteristics of the tools/frameworks in each of these categories:

Within the big data landscape there are multiple approaches to accessing, analyzing, and manipulating data in Hadoop. Each depends on key considerations such as latency, ANSI SQL completeness (and the ability to tolerate machine-generated SQL), developer and analyst skillsets, and architecture tradeoffs.

The usual suspects are included: Hive, Impala, Preso, Spark/Shark, Drill.

sql-on-hadoop-segments-diagram

Original title and link: SQL on Hadoop: An overview of frameworks and their applicability (NoSQL database©myNoSQL)

via: http://www.mapr.com/products/sql-on-hadoop-details


Everything is faster than Hive

Derrick Harris has brought together a series of benchmarks conducted by the different SQL-on-Hadoop implementors comparing their solution (Impala, Stinger/Tez, HAWQ, Shark) with

For what it’s worth, everyone is faster than Hive — that’s the whole point of all of these SQL-on-Hadoop technologies. How they compare with each other is harder to gauge, and a determination probably best left to individual companies to test on their own workloads as they’re making their own buying decisions. But for what it’s worth, here is a collection of more benchmark tests showing the performance of various Hadoop query engines against Hive, relational databases and, sometimes, themselves.

As Derrick Harris remarks, the only direct comparisons are between HAWQ and Impala (and this seems to be old as it mentions Impala being in beta) and the benchmark run by AMPlab (the guys behind Shark) comparing Redshift, Hive, Shark, and Impala.

The good part is that both the Hive Testbench and AMPlab benchmark are available on GitHub.

Original title and link: Everything is faster than Hive (NoSQL database©myNoSQL)

via: http://gigaom.com/2014/01/13/cloudera-says-impala-is-faster-than-hive-which-isnt-saying-much/


Spark and Shark company Databricks raises $14M from Andreessen Horowitz

Spark and Shark getting wings:

A team of professors who has created the in-memory Spark and Shark platforms for analyzing big data has raised nearly $13.9 million to commercialize those products. The company is still in stealth mode, but it’s called Databricks and Andreessen Horowitz led the round. […] It also lists Databricks’ very impressive board of directors: Co-founder and CEO Ion Stoica (University of California, Berkeley professor and former co-founder and CEO of Conviva); Co-founder and CTO Matei Zaharia (MIT professor); Ben Horowitz (general partner at Andreessen Horowitz and former Opsware co-founder and CEO); and Scott Shenker (University of California, Berkeley professor and former Nicira co-founder and CEO).

You should have probably heard already of all these guys.

Original title and link: Spark and Shark company Databricks raises $14M from Andreessen Horowitz (NoSQL database©myNoSQL)

via: http://gigaom.com/2013/09/25/databricks-raises-14m-from-andreessen-horowitz-wants-to-take-on-mapreduce-with-spark/