Apache Avro™ 1.7.7 Documentation. Introduction Apache Avro™ is a data serialization system. Avro provides: Rich data structures. A compact, fast, binary data format. A container file, to store persistent data. Schemas Avro relies on schemas. When Avro data is stored in a file, its schema is stored with it, so that files may be processed later by any program.
When Avro is used in RPC, the client and server exchange schemas in the connection handshake. Avro schemas are defined with JSON . Comparison with other systems Avro provides functionality similar to systems such as Thrift, Protocol Buffers, etc. Dynamic typing: Avro does not require that code be generated. Apache Avro, Avro, Apache, and the Avro and Apache logos are trademarks of The Apache Software Foundation. Welcome to Apache Flume — Apache Flume. Oozie - Apache Oozie Workflow Scheduler for Hadoop. Apache ZooKeeper - Home. Hadoop YARN. Apache™ Hadoop® YARN is a sub-project of Hadoop at the Apache Software Foundation introduced in Hadoop 2.0 that separates the resource management and processing components. YARN was born of a need to enable a broader array of interaction patterns for data stored in HDFS beyond MapReduce.
The YARN-based architecture of Hadoop 2.0 provides a more general processing platform that is not constrained to MapReduce. As part of Hadoop 2.0, YARN takes the resource management capabilities that were in MapReduce and packages them so they can be used by new engines. This also streamlines MapReduce to do what it does best, process data. With YARN, you can now run multiple applications in Hadoop, all sharing a common resource management. When enterprise data is made available in HDFS, it is important to have multiple ways to process that data.
What YARN Does YARN enhances the power of a Hadoop compute cluster in the following ways: How YARN Works. Hortonworks. We Do Hadoop. Kite Software Development Kit. Apache Pig. HDFS Explorer (Win) Impala. Cloudera Impala is the industry’s leading massively parallel processing (MPP) SQL query engine that runs natively in Apache Hadoop. The Apache-licensed, open source Impala project combines modern, scalable parallel database technology with the power of Hadoop, enabling users to directly query data stored in HDFS and Apache HBase without requiring data movement or transformation. Impala is designed from the ground up as part of the Hadoop ecosystem and shares the same flexible file and data formats, metadata, security and resource management frameworks used by MapReduce, Apache Hive, Apache Pig and other components of the Hadoop stack.
Now You Have a Choice Before Impala, if your relational database was at capacity, you may have had no choice but to expand that system to maintain your expectations of performance. If you were using Hadoop to affordably analyze any amount or kind of data, but wanted interactive performance, you had to move that data into a fast relational database. Apache Hive TM. Hadoop. Pangool - Hadoop API made easy. Splout SQL. Apache ZooKeeper. Spark. HBase. Ambari.