How-to Guides for Confluent Cloud for Apache Flink

Discover how Confluent Cloud for Apache Flink® can help you accomplish common processing tasks such as joins and aggregations. This section provides step-by-step guidance on how to use Flink to process your data efficiently and effectively.

• Aggregate a Stream in a Tumbling Window

• Combine Streams and Track Most Recent Records

• Compare Current and Previous Values in a Data Stream

• Convert the Serialization Format of a Topic

• Create a User Defined Function

• Handle Multiple Event Types

• Read Records without a Schema ID Prefix

• Resolve Statement Issues

• Run a Snapshot Query

• Scan and Summarize Tables

• View Time Series Data

Flink actions

Confluent Cloud for Apache Flink provides Flink Actions that enable you to perform specific data-processing tasks on topics with minimal configuration. These actions simplify common workloads with a user-friendly interface that lets you configure and execute them.

• Create an Embedding: Convert data in a topic’s column into a vector embedding for AI model inference.

• Deduplicate Rows in a Table: Remove duplicate records from a topic based on specified fields, ensuring that only unique records are retained in the output topic.

• Mask Fields in a Table: Mask sensitive data in specified fields of a topic by replacing the original data with a static value.

• Transform a Topic: Change a topic’s properties by applying custom Flink SQL transformations.

Related content

• Video: How to Set Idle Timeouts

• Video: How to Analyze Data from a REST API with Flink SQL

• Video: How To Use Streaming Joins with Apache Flink

• Video: How to Visualize Real-Time Data from Apache Kafka using Apache Flink SQL and Streamlit

• Use Flink SQL with Kafka, Streamlit, and the Alpaca API

Note

This website includes content developed at the Apache Software Foundation under the terms of the Apache License v2.