Many companies have a single Hive Metastore service instance in their production to manage all of their metadata, either Hive metadata or non-Hive metadata, as the source of truth. Apache Spark™ : The faster new execution engine for Apache Hive from Cloudera on Vimeo. Hive provides a mechanism to project structure onto this data and query the data using a SQL-like language called HiveQL.

HiveCatalog; HiveCatalog. Using the HiveCatalog and Flink’s connector to Hive, Flink can read and write from Hive data as an alternative to Hive’s batch engine.

Apache Hive data warehouse software facilitates querying and managing large datasets residing in distributed storage. Reading & Writing Hive Tables; Reading & Writing Hive Tables. Downloadable formats including Windows Help format and offline-browsable html are available from our distribution mirrors. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed.

Apache HTTP Server Documentation¶ The documentation is available is several formats.

Hive Tables.

The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large data sets. Apache Hadoop 3.2.1. Online browsable documentation is also available: Version 2.4 .

Users can run batch processing workloads with Hive while also analyzing the same data for interactive SQL or machine-learning workloads using tools like Apache Impala or Apache Spark—all within a single platform. Specifying storage format for Hive tables; Interacting with Different Versions of Hive Metastore; Spark SQL also supports reading and writing data stored in Apache Hive. Partnered with the ecosystem Seamlessly integrate with the tools your business already uses by leveraging Cloudera’s 2,600+ partner ecosystem. Be sure to follow the instructions to include the correct dependencies in your application. Internally, Spark SQL uses this extra information to perform extra optimizations. Using the Hive query language (HiveQL), which is very similar to SQL, queries are converted into a series of jobs that execute on a Hadoop cluster through MapReduce or Apache Spark. Version 2.2 (Historical) Version 2.0 (Historical) Version 1.3 (Historical) Apache Hadoop 3.2.1 incorporates a number of significant enhancements over the previous major release line (hadoop-3.2).

Apache Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. This release is generally available (GA), meaning that it represents a point of API stability and quality that we consider production-ready. Hive Metastore has evolved into the de facto metadata hub over the years in Hadoop ecosystem. However, since Hive has a large number of dependencies, these dependencies are not included in the default Spark distribution. Spark SQL is a Spark module for structured data processing.