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This example shows how to read data from a Kafka stream. You can then perform transformations and write the processed data to a storage location or another system. **Spark Structured Streaming** provides a powerful and efficient framework for handling streaming data in Databricks. With Databricks, you can easily integrate with various streaming sources and output data to various destinations, such as Delta Lake or other data stores. This seamless integration ensures real-time or near real-time data processing and analytics. This makes Databricks an excellent choice for applications requiring timely insights. By utilizing Databricks and Spark Structured Streaming, organizations can build robust and scalable streaming data pipelines. This helps them derive value from real-time data and make data-driven decisions swiftly.
