Apache Flink


Apache Flink is an open-source data processing framework for building real-time and batch processing pipelines. It can perform various tasks such as data streaming, event-driven applications, batch processing along with a unified API.


The Flink architecture consists of the following components:

  • JobManager: It coordinates the distributed execution of Flink applications.
  • TaskManagers: Data processing tasks are executed by TaskManagers, with each task manager running one or more data processing pipelines.
  • Data Stream APIs: It provides for data processing and processing stream-oriented data.
  • Data Set APIs: It provides for data set-oriented processing such as batch.
  • Flink Libraries: Flink comes with prepackaged libraries such as Gelly and Table API for graph processing and data analysis.

Apache Flink offers several benefits that make it a popular choice for big data processing projects:

  • Real-time processing: Flink provides support for real-time streaming and makes it easier for developers to write stream processing applications.
  • Scalability: Flink is highly scalable and designed to handle large volumes of data processing workloads.
  • Fault tolerance: Flink can automatically recover from failures in the system without losing any data.
  • Unified API: With a unified API, Flink provides a common interface for both streaming and batch data processing tasks.
  • Integration with other big data technologies: Flink integrates with other big data technologies like Hadoop and can also work with other open-source big data technologies like Apache Cassandra and Apache Kafka.

Apache Flink competes with other popular data processing frameworks, such as Apache Spark and Apache Storm. Each of these data processing frameworks has its own strengths and weaknesses, making the choice of which to use highly dependent on individual use cases. Some of the main differences between Flink and other data processing frameworks are:

  • Real-time processing capabilities: Flink is better at handling real-time processing.
  • Iterative processing: Flink is better at iterative processing for machine learning workloads.
  • Streaming: Apache Storm provides better support for continuous streaming whereas Flink is more suitable for batch and stream processing.

Use Cases

Some of the common use cases of Apache Flink are:

  • Real-time analytics and monitoring of streaming data.
  • Large-scale batch processing.
  • Event-driven applications.
  • Iterative machine learning processes.
  • Processing complex event streams and building complex data pipelines.


Apache Flink is a versatile and powerful data processing framework that offers various features for both batch and real-time data processing. It provides scalability, fault tolerance, and unified APIs, making it a popular choice for big data processing projects.

If you are a Dremio user, Apache Flink can help you in building real-time and batch data processing pipelines for your data lakehouse environment.

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