Apache Beam vs Apache Spark: What are the differences?
Apache Beam: A unified programming model. It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments; Apache Spark: Fast and general engine for large-scale data processing. Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Apache Beam can be classified as a tool in the "Workflow Manager" category, while Apache Spark is grouped under "Big Data Tools".
Apache Spark is an open source tool with 22.9K GitHub stars and 19.7K GitHub forks. Here's a link to Apache Spark's open source repository on GitHub.
Uber Technologies, Slack, and Shopify are some of the popular companies that use Apache Spark, whereas Apache Beam is used by Handshake, Skry, Inc., and Reelevant. Apache Spark has a broader approval, being mentioned in 356 company stacks & 564 developers stacks; compared to Apache Beam, which is listed in 9 company stacks and 4 developer stacks.
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What is Apache Beam?
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