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Shark is a large-scale data warehouse system for Spark designed to be compatible with Apache Hive. It can execute Hive QL queries up to 30 times faster than Hive without any modification to the existing data or queries.
Shark supports Hive's query language, metastore, serialization formats, and user-defined functions, providing seamless integration with existing Hive deployments and a familiar, more powerful option for new ones.
There is a small corpus of helpful documents on Shark. The shark-users mailing list is also very active and will be a helpful resource for beginners.
Running Shark Locally: Get Shark up and running on a single node for a quick spin in ~ 5 mins.
Running Shark on EC2: Launch a Shark cluster on Amazon EC2 in ~ 10 mins, including examples on how to query data in S3.
Running Shark on a Cluster: Get Shark up and running on your own cluster.
Shark User Guide: An introduction to running Shark and its API.
Building Shark from Source Code
Compatibility with Apache Hive: Deploying Shark in existing Hive Warehouses.
Shark Release 0.2 - Oct 15, 2012
Developer Guide: For people who are interested in contributing. We don't have much here yet, but plan to add more content soon.
Startup Tasks for New Contributors
Hive Patches: Patches we made to Hive.
Spark: The in-memory cluster computing framework that powers Shark.