[DEPRECATED] Kubernetes operator for managing the lifecycle of Apache Flink and Beam applications.
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Updated
Sep 2, 2022 - Go
[DEPRECATED] Kubernetes operator for managing the lifecycle of Apache Flink and Beam applications.
The Apache Flink SQL Cookbook is a curated collection of examples, patterns, and use cases of Apache Flink SQL. Many of the recipes are completely self-contained and can be run in Ververica Platform as is.
This library allows Scala and Java-based projects (including Apache Flink, Apache Hive, Apache Beam, and PrestoDB) to read from and write to Delta Lake.
Yet Another UserAgent Analyzer
Elastic data processing with Apache Pulsar and Apache Flink
Distributed Temporal Graph Analytics with Apache Flink
Low-code tool for automating actions on real time data | Stream processing for the users.
flink-tensorflow - TensorFlow support for Apache Flink
A complete example of a big data application using : Kubernetes (kops/aws), Apache Spark SQL/Streaming/MLib, Apache Flink, Scala, Python, Apache Kafka, Apache Hbase, Apache Parquet, Apache Avro, Apache Storm, Twitter Api, MongoDB, NodeJS, Angular, GraphQL
Kubernetes operator for managing the lifecycle of Apache Flink and Beam applications.
Framework for Apache Flink unit tests
A data generator source connector for Flink SQL based on data-faker.
A tool that help automate deployment to an Apache Flink cluster
FeatHub - A stream-batch unified feature store for real-time machine learning
Dagger is an easy-to-use, configuration over code, cloud-native framework built on top of Apache Flink for stateful processing of real-time streaming data.
flink-sql 在 flink 上运行 sql 和 构建数据流的平台 基于 apache flink 1.10.0
flink-jpmml is a fresh-made library for dynamic real time machine learning predictions built on top of PMML standard models and Apache Flink streaming engine
Streaming Synthetic Sales Data Generator: Streaming sales data generator for Apache Kafka, written in Python
Examples for using Apache Flink® with DataStream API, Table API, Flink SQL and connectors such as MySQL, JDBC, CDC, Kafka.
Stream processing guidelines and examples using Apache Flink and Apache Spark
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