Apache Bigtop Integrations

Jira is the only project management tool you need to plan and track work across every team. Jira by Atlassian is the #1 software development tool for teams planning and building great products. Trusted by thousands of teams, Jira offers access to a wide range of tools for planning, tracking, and releasing world-class software, capturing and organizing issues, assigning work, and following team activity. It also integrates with leading developer tools for end-to-end traceability. From short projects, to large cross-functional programs, Jira helps break big ideas down into achievable steps. Organize work, create milestones, map dependencies and more. Link work to goals so everyone can see how their work contributes to company objectives and stay aligned to what’s important. Your next move, suggested by AI. Atlassian Intelligence takes your big ideas and automatically suggests the tasks to help get it done.

The leading open source automation server, Jenkins provides hundreds of plugins to support building, deploying and automating any project. As an extensible automation server, Jenkins can be used as a simple CI server or turned into the continuous delivery hub for any project. Jenkins is a self-contained Java-based program, ready to run out-of-the-box, with packages for Windows, Linux, macOS and other Unix-like operating systems. Jenkins can be easily set up and configured via its web interface, which includes on-the-fly error checks and built-in help. With hundreds of plugins in the Update Center, Jenkins integrates with practically every tool in the continuous integration and continuous delivery toolchain. Jenkins can be extended via its plugin architecture, providing nearly infinite possibilities for what Jenkins can do. Jenkins can easily distribute work across multiple machines, helping drive builds, tests and deployments across multiple platforms faster.

Beats is a free and open platform for single-purpose data shippers. They send data from hundreds or thousands of machines and systems to Logstash or Elasticsearch. Beats are open source data shippers that you install as agents on your servers to send operational data to Elasticsearch. Elastic provides Beats for capturing data and event logs. Beats can send data directly to Elasticsearch or via Logstash, where you can further process and enhance the data, before visualizing it in Kibana. Want to get up and running quickly with infrastructure metrics monitoring and centralized log analytics? Try out the Metrics app and the Logs app in Kibana. For more details, see Analyze metrics and Monitor logs. Whether you’re collecting from security devices, cloud, containers, hosts, or OT, Filebeat helps you keep the simple things simple by offering a lightweight way to forward and centralize logs and files.

Use Apache HBase™ when you need random, realtime read/write access to your Big Data. This project's goal is the hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware. Automatic failover support between RegionServers. Easy to use Java API for client access. Thrift gateway and a REST-ful Web service that supports XML, Protobuf, and binary data encoding options. Support for exporting metrics via the Hadoop metrics subsystem to files or Ganglia; or via JMX.

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures. A wide variety of companies and organizations use Hadoop for both research and production. Users are encouraged to add themselves to the Hadoop PoweredBy wiki page. Apache Hadoop 3.3.4 incorporates a number of significant enhancements over the previous major release line (hadoop-3.2).

Apache Spark™ is a unified analytics engine for large-scale data processing. Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells. Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources.