In an earlier blog post, Democratizing Analytics within Kafka With 3 Powerful New Access Patterns in HDP and HDF, we discussed different access patterns that provide application developers and BI analysts with powerful new tools to implement diverse use cases where Kafka is a key component of their application architectures. In this post, we will discuss in detail the streaming access pattern and the addition of Kafka Streams support in HDF 3.3 and the upcoming HDP 3.1 release. Before the addition of Kafka Streams support, HDP and HDF supported two stream processing engines: Spark Structured Streaming and Streaming Analytics Manager (SAM) with Storm. So naturally, this begets the following question: why add a third stream processing engine to the platform?
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big data,apache kafka,stream processing,kafka streams
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big data,apache kafka,stream processing,kafka streams
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