Big Data open source application building tools
Job scheduling tool
Hera distributed task scheduling system
hera distributed task scheduling system big data system task scheduling task scheduling (data sector-specific)
hera distributed scheduling system is the second development based on open source scheduling system before Ali (zeus), which zeus probably open in 2014, but did not open after maintenance. Our company (two-dimensional fire) in 2015 introduced the zeus task scheduling system, it has been used to November this year, in our department and the whole company plays an irreplaceable role. I use zeus this year, I had to admit that it's powerful, as long as the cluster size appropriate to the configuration, he can take thousands or even tens of thousands or even higher order of magnitude of the task scheduling. However, because the code is not maintained zeus front end with GWT technology it is difficult to maintain in zeus above. I was with another junior partner (nickname: Peak, now Ali Taobao department) in March this year to rewrite zeus, change Ming Hela (hera).
EasyScheduler workflow scheduling system
EasyScheduler project documentation
Easy Scheduler is a workflow scheduling system, mainly to solve complex data dependencies ETL development, but can not directly monitor the health status of tasks and other issues. Easy Scheduler to DAG streaming manner Task assembled, can run real-time monitoring tasks, while supporting retry, fail to recover, pause and Kill tasks and other operations from the specified node.
Lean operational tools
Analysys Ark
Analysys Ark Argo, is a privatization of the deployment, open and free of user analysis and lean operations products.
The main products for the fledgling small amount of data, with its own technology and product innovation teams or individuals practical ability.
Support privatization deployment, includes data analysis, user grouping, closed-loop operation, maximum extent to meet the needs.
make your business quickly and inexpensively build up support for a second level of data queries ten billion intelligent data platform.
invest less R & D resources, time and maintenance costs, ease of use, outstanding obtain user data analysis products