Apache Spark Streaming
Scalable, high-throughput, fault-tolerant stream processing of live data streams.
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.
āØ 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)
šÆ 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.
šÆ Use Cases (5)
ā Best For
- Netflix's real-time data processing and analytics
- Uber's real-time data analytics
- Pinterest's real-time analytics
š” Check With Vendor
Verify these considerations match your specific requirements:
- Applications requiring true event-at-a-time processing with very low latency.
š Alternatives
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.
š» Platforms
š Integrations
š° Pricing
Free tier: Open-source, free to use.
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