PySpark vs Apache Spark: What are the differences?
What is PySpark? The Python API for Spark. It is the collaboration of Apache Spark and Python. it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data.
What is 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.
PySpark can be classified as a tool in the "Data Science Tools" 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 PySpark is used by Repro, Autolist, and Shuttl. Apache Spark has a broader approval, being mentioned in 356 company stacks & 564 developers stacks; compared to PySpark, which is listed in 8 company stacks and 6 developer stacks.
What is PySpark?
What is Apache Spark?
Why do developers choose PySpark?
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Spark is good at parallel data processing management. We wrote a neat program to handle the TBs data we get everyday.