mirror of https://github.com/apache/druid.git
22 lines
1.1 KiB
Markdown
22 lines
1.1 KiB
Markdown
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layout: doc_page
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Druid vs Spark
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==============
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We are not experts on Spark, if anything is incorrect about our portrayal, please let us know on the mailing list or via some other means.
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Spark is a cluster computing framework built around the concept of Resilient Distributed Datasets (RDDs) and
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can be viewed as a back-office analytics platform. RDDs enable data reuse by persisting intermediate results
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in memory and enable Spark to provide fast computations for iterative algorithms.
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This is especially beneficial for certain work flows such as machine
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learning, where the same operation may be applied over and over
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again until some result is converged upon. Spark provides analysts with
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the ability to run queries and analyze large amounts of data with a
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wide array of different algorithms.
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Druid is designed to power analytic applications and focuses on the latencies to ingest data and serve queries
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over that data. If you were to build a web UI where users could
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arbitrarily explore data, the latencies seen by using Spark may be too slow for interactive use cases.
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