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PySpark Integration with Apache Kafka

Understand the key concepts of Apache Kafka and how it supports high-throughput, scalable, real-time data streaming. Learn to integrate Kafka with PySpark using Structured Streaming to ingest and process streaming data efficiently. This lesson helps you manage live data streams and build fault-tolerant data pipelines using PySpark and Kafka.

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Apache Kafka is an open-source distributed streaming platform designed for handling real-time data with high-throughput and low-latency capabilities.

Its core features include:

  • High throughput: Kafka can deliver messages at network-limited throughput using a cluster with minimal latency.
  • Scalability: The platform scales smoothly to accommodate clusters with up to a thousand brokers, managing trillions of messages daily and petabytes of data.
  • Storage: Kafka securely stores streams of data in a distributed, durable, and fault-tolerant cluster.
  • Availability: It ensures robust connectivity across separate clusters, spanning various geographic locations.

Kafka architecture

Kafka operates as an event streaming platform, integrating three critical capabilities for end-to-end event streaming solutions:

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