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databricks-spark-structured-streaming

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Build robust streaming pipelines.

Data & Analytics#streaming#real-time data#kafka#data pipelines#databricks#delta lake#spark structured streaming
AuthorAradhya0510
Version1.0.0
Installs0

System Documentation

What problem does it solve?

This Skill provides comprehensive guidance and patterns for building reliable, production-ready streaming data pipelines using Spark Structured Streaming on Databricks.

Core Features & Use Cases

  • End-to-End Pipelines: Covers ingestion from Kafka, processing, stateful operations, and writing to Delta.
  • Best Practices: Includes advice on checkpointing, triggers, monitoring, and error handling.
  • Use Case: Implement a real-time analytics pipeline that ingests clickstream data from Kafka, enriches it with user dimension data, performs sessionization, and writes aggregated results to Delta tables, all while ensuring exactly-once semantics.

Quick Start

Use the databricks-spark-structured-streaming skill to set up a Kafka to Delta streaming pipeline with checkpointing.

Dependency Matrix

Required Modules

None required

Components

references

💻 Claude Code Installation

Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.

Please help me install this Skill:
Name: databricks-spark-structured-streaming
Download link: https://github.com/Aradhya0510/databricks-cv-accelerator/archive/main.zip#databricks-spark-structured-streaming

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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