Databricks provides robust integration with Apache Kafka, allowing you to efficiently ingest, process, and analyze streaming data in real time.

Key features and benefits:

• Seamless connectivity: Easily connect to Kafka clusters, whether on-premises or in the cloud, using Databricks’ Kafka connector.
• Structured Streaming: Leverage Spark Structured Streaming to process Kafka data in a scalable and fault-tolerant manner.
• Delta Lake integration: Store processed Kafka data in Delta Lake for reliable storage and efficient querying.
• SQL and Python APIs: You can use familiar SQL or Python APIs to read, transform, and write Kafka data.
• Real-time analytics: Perform real-time analytics on streaming Kafka data to gain immediate insights.
• End-to-end streaming pipelines: Build end-to-end pipelines that ingest data from Kafka, process it, and deliver results to various destinations.

Resources:

• Stream processing with Apache Kafka and Databricks: https://docs.databricks.com/en/connect/streaming/kafka.html
• Delta Live Tables with Apache Kafka – Databricks: https://www.databricks.com/resources/demos/videos/data-engineering/delta-live-tables-with-apache-kafka
• Get data into Databricks from Kafka: https://www.databricks.com/resources/demos/videos/ingestion/get-data-into-databricks-from-kafka.