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Apache Spark Streaming

Scalable, high-throughput, fault-tolerant stream processing of live data streams.

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Overview

Spark Streaming is an extension of the core Spark API that enables scalable, high-throughput, fault-tolerant stream processing of live data streams. Data can be ingested from many sources like Kafka, Kinesis, or TCP sockets, and can be processed using complex algorithms expressed with high-level functions like map, reduce, join and window.

✨ Key Features

  • Micro-batch processing
  • Integration with the Spark ecosystem (SQL, MLlib, GraphX)
  • Fault tolerance
  • Stateful stream processing
  • Unified API for batch and streaming (with Structured Streaming)

🎯 Key Differentiators

  • Tight integration with the broader Spark ecosystem
  • Unified API for batch and streaming
  • Large and active community

Unique Value: A powerful and scalable stream processing framework that is tightly integrated with the popular Apache Spark ecosystem, enabling unified batch and streaming applications.

🎯 Use Cases (5)

Real-time ETL Streaming analytics Real-time machine learning Log processing Data enrichment

✅ Best For

  • Netflix's real-time data processing and analytics
  • Uber's real-time data analytics
  • Pinterest's real-time analytics

💡 Check With Vendor

Verify these considerations match your specific requirements:

  • Applications requiring true event-at-a-time processing with very low latency.

🏆 Alternatives

Apache Flink Apache Storm Google Cloud Dataflow

Uses a micro-batching approach, which can result in slightly higher latency compared to true streaming engines like Flink, but offers excellent throughput and integration with Spark's other libraries.

💻 Platforms

Linux macOS Windows

🔌 Integrations

Apache Kafka Amazon Kinesis HDFS and various other data sources

💰 Pricing

Contact for pricing
Free Tier Available

Free tier: Open-source, free to use.

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