databricks-spark-structured-streaming

Spark Structured Streaming builds real-time data processing pipelines by reading event data, applying continuous transformations, and writing fault-tolerant results to Delta Lake. It handles streaming analytics efficiently for production-grade workloads.

Spark Structured Streaming integrates with Kafka for real-time data ingestion. You can configure a streaming pipeline to read continuous event data directly from Kafka topics, process it through Spark, and output the results to Delta Lake.

Delta Lake provides fault tolerance and data integrity for Structured Streaming pipelines. It serves as the underlying storage layer to ensure exactly-once processing and reliable output management for streaming data.

Processing real-time transactions with Spark involves creating a Structured Streaming pipeline. The pipeline reads transaction events from Kafka, applies real-time analytics transformations, and writes the aggregated results to Delta Lake for reporting.

Running Spark Structured Streaming pipelines requires Apache Spark 3.0 or higher and Delta Lake. These dependencies provide the necessary framework architecture to support scalable, fault-tolerant real-time data processing.