- Apache Flink Tutorial
- Apache Flink - Home
- Apache Flink - Big Data Platform
- Batch vs Real-time Processing
- Apache Flink - Introduction
- Apache Flink - Architecture
- Apache Flink - System Requirements
- Apache Flink - Setup/Installation
- Apache Flink - API Concepts
- Apache Flink - Table API and SQL
- Creating a Flink Application
- Apache Flink - Running a Flink Program
- Apache Flink - Libraries
- Apache Flink - Machine Learning
- Apache Flink - Use Cases
- Apache Flink - Flink vs Spark vs Hadoop
- Apache Flink - Conclusion
- Apache Flink Resources
- Apache Flink - Quick Guide
- Apache Flink - Useful Resources
- Apache Flink - Discussion
Apache Flink - Conclusion
The comparison table that we saw in the previous chapter concludes the pointers pretty much. Apache Flink is the most suited framework for real-time processing and use cases. Its single engine system is unique which can process both batch and streaming data with different APIs like Dataset and DataStream.
It does not mean Hadoop and Spark are out of the game, the selection of the most suited big data framework always depends and vary from use case to use case. There can be several use cases where a combination of Hadoop and Flink or Spark and Flink might be suited.
Nevertheless, Flink is the best framework for real time processing currently. The growth of Apache Flink has been amazing and the number of contributors to its community is growing day by day.
Happy Flinking!