DeltaStream, powered by Apache Flink, processes streaming data from sources like Kafka and Kinesis, allowing for real-time analysis and data preparation. The recent integration with Databricks enhances the platform by enabling users to write results directly to Delta Lake, which is part of Databricks’ Lakehouse architecture. DeltaStream can handle latency-sensitive applications by continuously processing streaming data and updating Delta Tables in real-time, providing a perfect fit for tasks requiring both streaming and batch processing capabilities, such as alerting on fraudulent activities. The integration simplifies the process of managing streaming data in Databricks, allowing users to transform and prepare data with a single SQL query before loading it into the platform. This setup is beneficial for data scientists who need to perform historical analysis or business insights on streaming data. Using DeltaStream, users can easily create and manage Databricks Tables through a continuous query that keeps data up-to-date in the Delta Lake, demonstrating a seamless interaction between streaming data and Databricks' batch processing capabilities.