Hi all: The Apache DolphinScheduler is pleased to announce the release of the Apache DolphinScheduler 2.0.1.
Apache DolphinScheduler is a cloud-native visual Big Data workflow scheduler system dedicated to solving the complex task dependencies in data processing, making the scheduler system out of the box for data processing. In this major version release, DolphinScheduler has undergone a microkernel + plug-in architecture improvement, 70% of the code has been refactored, and the long-awaited plug-in function has also been optimized. At the same time, related optimizations have been made for some user experience issues. Download Links: https://dolphinscheduler.apache.org/en-us/download/download.html Release Notes: https://github.com/apache/dolphinscheduler/releases/tag/2.0.1 If you have any questions, please refer to DolphinScheduler Resources: - Official website: https://dolphinscheduler.apache.org/ - GitHub:https://github.com/apache/dolphinscheduler - Issue: https://github.com/apache/dolphinscheduler/issues/ - Mailing list: [email protected] - Documents: https://dolphinscheduler.apache.org/en-us/docs/latest/user_doc/quick-start.html *About DolphinScheduler:* Apache DolphinScheduler is a cloud-native visual Big Data workflow scheduler system. As a distributed and extensible data workflow scheduler platform with rich directed acyclic graph (DAG) visual interfaces, DolphinScheduler solves complex task dependencies and triggers in the data pipeline. Out-of-the-box, its easy-to-extend processing connects numerous systems to 600,000-level data task scheduling. Apache DolphinScheduler is: - Cloud Native — support multi-cloud/data center workflow management, also supports Kubernetes, Docker deployment and custom task types, distributed scheduling, with overall scheduling capability, increased linearly with the scale of the cluster - Support multi-tenant - Highly Reliable — decentralized multi-master and multi-worker, high availability, supported by itself, overload processing - User-Friendly — all process definition operations are visualized, defines key information at a glance, one-click deployment - Supports Rich Scenarios — includes streaming, pause, recover operation, and additional task types such as Spark, Hive, MR, Shell, Python, Flink, Sub_process, and more. Best Regards! - Apache DolphinScheduler Team -- BaoLiang Apache DolphinScheduler
