databricks-spark-structured-streaming

What problem does it solve?

Spark Structured Streaming in production can be complex and brittle, making it hard to guarantee reliability and performance. This guide provides a structured approach to building robust streaming pipelines, applying stateful processing, and optimizing throughput, latency, and fault tolerance.

Core Features & Use Cases

• Patterns for Kafka ingestion, stream-to-Delta writes, stream-stream joins, and windowed analytics.
• Production best practices covering watermarking, state store tuning, triggers, and monitoring for real-world workloads.
• Real-world use cases including real-time dashboards, event-driven ETL, and streaming analytics at scale.

Quick Start

Create a minimal Spark Structured Streaming job that reads from Kafka, applies a watermark, and writes to Delta.