spark-structured-streaming

Name: spark-structured-streaming
Availability: InStock
Author: laurentprat-db

Build Spark Structured Streaming pipelines from Kafka to Delta Lake.

Updated Feb 27, 2026

Search Tags:#real-time data #kafka #data pipelines #spark streaming #delta lake #structured streaming

Ready to run

npx skills add https://github.com/LaurentPRAT-DB/LPT_claude_config --skill spark-structured-streaming

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: spark-structured-streaming
Source: https://github.com/LaurentPRAT-DB/LPT_claude_config/tree/main/skills/spark-structured-streaming
Command: npx skills add https://github.com/LaurentPRAT-DB/LPT_claude_config --skill spark-structured-streaming

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides comprehensive guidance and patterns for building production-ready streaming data pipelines using Spark Structured Streaming, enabling real-time data processing and analysis.

Core Features & Use Cases

Real-time Data Ingestion: Connect to sources like Kafka and ingest data into Delta Lake.
Stateful Operations: Implement complex logic like stream-stream joins, windowed aggregations, and deduplication.
Production Optimization: Learn best practices for checkpointing, watermarking, and performance tuning.
Use Case: Process clickstream data from Kafka in real-time, enrich it with user dimension data, and aggregate session activity into a Delta table for immediate dashboarding.

Quick Start

Use the spark-structured-streaming skill to build a Kafka to Delta pipeline with a 30-second trigger interval.

Frequently Asked Questions about spark-structured-streaming

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build a real-time data pipeline from Kafka to Delta Lake using Spark Structured Streaming?▼

To build real-time data pipelines with Spark Structured Streaming, connect a Kafka source, define a streaming read, and write the continuous data into a Delta Lake table using a configured trigger interval like 30 seconds.

What's the best way to implement stream-stream joins and windowed aggregations in Spark Streaming?▼

Implementing stateful operations like stream-stream joins and windowed aggregations in Spark Streaming requires defining watermarks to manage state and ensure accurate event-time processing across unbounded data streams.

How do I optimize Spark Structured Streaming performance for production workloads?▼

Optimize Spark Structured Streaming performance for production workloads by applying best practices for checkpointing, configuring watermarks to drop late data, and tuning trigger intervals to balance latency and throughput.

Can I process and enrich real-time clickstream data with user dimension data in Spark Streaming?▼

Yes, you can process real-time clickstream data in Spark Streaming by ingesting events from Kafka, enriching them with user dimension data, and aggregating session activity into a Delta table for immediate dashboarding.

When do I need watermarking and checkpointing in Spark Structured Streaming pipelines?▼

Watermarking and checkpointing are needed in Spark Structured Streaming pipelines when handling stateful operations to prevent unbounded state growth and ensure fault recovery by tracking processed data across streaming batches.