Why Apache Flink?
Apache Flink is a processing engine enabling the definition of streaming data pipelines with SQL statements. It can work in batch or streaming mode, it’s distributed by default, and performs computation at in-memory speed at any scale.
You can use Flink to implement batch data processing, but also for handling real-time processing for data streams. More conventional database solutions might only have automation to process the accumulated data at certain intervals. Working with data streams, on the other hand, allows you to analyze and process the data in real time. This means that you can use Apache Flink to configure solutions for real-time alerting or triggering operations instead of using less efficient solutions.
Real time filtering
A key part of data stream processing is the ability to filter and transform incoming data in real time. As an example, compliance requirements might mean that you have to ensure limited visibility and access to certain data. In such cases, the capability to process incoming data directly can add significant value and efficiency. Using Apache Flink, you can configure data pipelines to handle the incoming data and store or deliver it differently according to the type of the data or content. You can also transform the data according to your needs, or combine sources to enrich the data.
Use familiar SQL
As Apache Flink uses SQL as a key part of constructing data pipelines, it is quite an approachable option for people who are already familiar with databases and batch processing. However, there are some relevant concepts (such as windows, watermarks, and checkpoints) that are worth knowing if you are new to data stream processing.