Apache Spark Streaming

About Apache Spark Streaming

Extension of Apache Spark API enabling scalable, high-throughput, fault-tolerant processing of live data streams. Integrated within Spark ecosystem for complex real-time data processing tasks.

Key Features

• 1Micro-batch and continuous streaming modes on the Spark engine
• 2Exactly-once semantics with checkpointing and write-ahead logs
• 3Unified API with Spark batch — same DataFrame operations on streams
• 4Native Kafka, Kinesis, S3, and Delta Lake source/sink support
• 5Watermarking for handling late-arriving data in event-time windows

How Python Data Engineers Use Apache Spark Streaming

Python data engineers use Spark Structured Streaming via PySpark to process high-volume Kafka streams at scale. A streaming job reads a Kafka topic as a DataFrame, applies transformations (filtering, aggregations, joins with static data), and writes results continuously to Delta Lake or a database — using the same PySpark syntax as batch jobs.