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Introduction

Like twins separated at birth, Apache Flink and Apache Spark have often been compared because both have been considered as replacements for MapReduce computing of big data.

Highlights

  • Flink is a streaming engine with aspirations paralleling those of Spark, but it will not displace Spark.
  • Flink’s big claim to fame will be its ability to perform time-stamping and check-pointing, allowing streams to be rolled back and replayed.
  • Apache Beam could make Flink far more accessible, because it will enable Flink’s streaming to be mixed and matched with analytics programs developed with other (better established) engines.

Features and Benefits

  • Differentiates Apache Flink from Spark and from other specialized streaming engines.
  • Pinpoints Flink's potential differentiator.

Key questions answered

  • What is Apache Flink?
  • Where does Apache Flink fit in the market for streaming and big data analytics?

Table of contents

Summary

  • Catalyst
  • Ovum view
  • Key messages

Recommendations

  • Recommendations for enterprises
  • Recommendations for vendors

Why another streaming engine?

  • IoT has forced the issue
  • What is Apache Flink?

Flink's potential aces in the hole

  • Flink is stateful
  • Apache Beam could shake things up
  • Commercial support

Appendix

  • Methodology
  • Further reading
  • Author

Recommended Articles

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