For some time Microsoft didn't offer a solution for processing big data in cloud environments. SQL Server is good for storage, but its ability to analyze terabytes of data is limited. Hadoop ...

Hive's SQL-like query language and vastly improved speed on huge data sets make it the perfect partner for an enterprise data warehouse Apache Hive is a tool built on top of Hadoop for analyzing ...

Apache Hadoop has been the driving force behind the growth of the big data industry. You'll hear it mentioned often, along with associated technologies such as Hive and Pig. But what does it do ...

Once you've created a Hadoop cluster within Amazon Web Services, you can process data using a hive script.

Hortonworks yesterday announced a new version of Apache Hive, the open source data warehouse software running on top of Hadoop, with new SQL query features and performance improvements.

Ashish Thusoo and Namit Jain explain how Facebook manages to deal with 12 TB of compressed new data everyday with Hive’s help. Hive is an open source data warehousing framework built on Hadoop ...

Hive follows the same SQL concepts like row, columns, and schema. Developers working on big data applications have a prevalent problem when reading Hadoop file systems data or Hive table data.

Commodity hardware is making waves as big data technology like Hadoop, Hive, Mahout, HBase and Cassandra make solving big data problems easier and simpler.

Facebook, for example, has over 30 petabytes of data in Hadoop clusters, and it created the Hive query front-end for Hadoop (which is now an Apache open-source project).

The Hive effort spearheaded by Thusoo and a small team inside Facebook grew quickly, and development of Hive continued well after he left with the company in 2011, along with another Facebook data ...