Aiven for Apache Flink® concepts

Aiven for Apache Flink® applications

An Aiven for Apache Flink® Application is an abstraction layer that

Built-in SQL editor

The built-in Table SQL editor in the [Aiven

Custom JARs in Aiven for Apache Flink®

Aiven for Apache Flink enables you to upload, deploy, and manage your own Java code as custom JARs within a JAR application.

Tables in Aiven for Apache Flink®

With Aiven for Apache Flink®, you can easily create and manage data

Checkpoints

Checkpoints in Aiven for Apache Flink® are a key feature for ensuring resiliency and fault tolerance in stateful functions.

Savepoints

Savepoints in Aiven for Apache Flink® are snapshots of the current state

Event and processing times

Event time refers to when events occur, and processing time is when a system observes or processes these events. Understanding the difference between these two is essential for data processing and streaming. It affects data handling, analysis, and storage.

Watermarks

Apache Flink® uses watermarks to synchronize and process events in data streams accurately. These watermarks are timestamps embedded in the data stream that track the progression of event time.

Windows

Apache Flink® uses the concept of windows to manage the continuous flow of data in streams by segmenting it into manageable chunks. This approach is essential due to the continuous and unbounded nature of data streams, where waiting for all data to arrive is impractical.

Standard and upsert connectors for Apache Kafka®

In addition to integration with Apache Kafka® through a standard

Settings for Apache Kafka® connectors

Explore the necessary settings for standard and upsert Kafka connectors in Aiven for Apache Flink®.