databricks-spark-structured-streaming

Name: databricks-spark-structured-streaming
Rating: 5 (9 reviews)
Author: gabrielmoreira

This comprehensive guide covers Spark Structured Streaming for production workloads. It details building streaming pipelines, Kafka ingestion, Real-Time Mode, triggers, stateful operations with watermarks, checkpoints, joins, and writing to multiple sinks.

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Updated last month

View on GitHub ↗License: MIT

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Spark Structured Streaming

Production-ready streaming pipelines with Spark Structured Streaming. This skill provides navigation to detailed patterns and best practices.

Quick Start

from pyspark.sql.functions import col, from_json

# Basic Kafka to Delta streaming
df = (spark
    .readStream
    .format("kafka")
    .option("kafka.bootstrap.servers", "broker:9092")
    .option("subscribe", "topic")
    .load()
    .select(from_json(col("value").cast("string"), schema).alias("dat

[Description truncada. Veja o README completo no GitHub.]

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