NoSQL Benchmarks NoSQL use cases NoSQL Videos NoSQL Hybrid Solutions NoSQL Presentations Big Data Hadoop MapReduce Pig Hive Flume Oozie Sqoop HDFS ZooKeeper Cascading Cascalog BigTable Cassandra HBase Hypertable Couchbase CouchDB MongoDB OrientDB RavenDB Jackrabbit Terrastore Amazon DynamoDB Redis Riak Project Voldemort Tokyo Cabinet Kyoto Cabinet memcached Amazon SimpleDB Datomic MemcacheDB M/DB GT.M Amazon Dynamo Dynomite Mnesia Yahoo! PNUTS/Sherpa Neo4j InfoGrid Sones GraphDB InfiniteGraph AllegroGraph MarkLogic Clustrix CouchDB Case Studies MongoDB Case Studies NoSQL at Adobe NoSQL at Facebook NoSQL at Twitter



Adopting Apache Hadoop and Hive

Moving Federal Gov analytics from MySQL to Hadoop and Hive:

HDFS offered us a distributed, resilient, and scalable filesystem while Hadoop promised to bring the work to where the data resided so we could make efficient use of local disk on multiple nodes. Hive, however, really pushed our decision in favor of a Hadoop-based system. Our data is just unstructured enough to make traditional RDBMS schemas a bit brittle and restrictive, but has enough structure to make a schema-less NoSQL system unnecessarily vague. Hive let us compromise between the two — it’s sort of a “SomeSQL” system.

Original title and link: Adopting Apache Hadoop and Hive (NoSQL databases © myNoSQL)