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Kibble: a suite of tools for collecting, aggregating and visualizing activity in software projects. Knox: a REST API Gateway for Hadoop Services; Kudu: a distributed columnar storage engine built for the Apache Hadoop ecosystem; Kvrocks: a distributed key-value NoSQL database, supporting the rich data structure; Kylin: distributed analytics engine
HBase is an open-source non-relational distributed database modeled after Google's Bigtable and written in Java.It is developed as part of Apache Software Foundation's Apache Hadoop project and runs on top of HDFS (Hadoop Distributed File System) or Alluxio, providing Bigtable-like capabilities for Hadoop.
Apache Hadoop (/ h ə ˈ d uː p /) is a collection of open-source software utilities for reliable, scalable, distributed computing.It provides a software framework for distributed storage and processing of big data using the MapReduce programming model.
High-availability cluster. Apache Mesos, from the Apache Software Foundation; Kubernetes, founded by Google Inc, from the Cloud Native Computing Foundation; Heartbeat, from Linux-HA
Apache Hive is a data warehouse software project. It is built on top of Apache Hadoop for providing data query and analysis. [3] [4] Hive gives an SQL-like interface to query data stored in various databases and file systems that integrate with Hadoop.
Apache Kudu is a free and open source column-oriented data store of the Apache Hadoop ecosystem. It is compatible with most of the data processing frameworks in the Hadoop environment. It provides completeness to Hadoop's storage layer to enable fast analytics on fast data.
Four bytes, ASCII 'O', 'b', 'j', followed by the Avro version number which is 1 (0x01) (Binary values 0x4F 0x62 0x6A 0x01). File metadata, including the schema definition. The 16-byte, randomly-generated sync marker for this file. For data blocks Avro specifies two serialization encodings: [6] binary and JSON. Most applications will use the ...
Cascading is a software abstraction layer for Apache Hadoop and Apache Flink. Cascading is used to create and execute complex data processing workflows on a Hadoop cluster using any JVM-based language (Java, JRuby, Clojure, etc.), hiding the underlying complexity of MapReduce jobs. It is open source and available under the Apache License.