Pig vs Apache Spark: What are the differences?
Developers describe Pig as "Platform for analyzing large data sets". Pig is a dataflow programming environment for processing very large files. Pig's language is called Pig Latin. A Pig Latin program consists of a directed acyclic graph where each node represents an operation that transforms data Operations are of two flavors: (1) relational-algebra style operations such as join, filter, project; (2) functional-programming style operators such as map, reduce. . On the other hand, Apache Spark is detailed as "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.
Pig and Apache Spark can be primarily classified as "Big Data" tools.
Pig and Apache Spark are both open source tools. It seems that Apache Spark with 22.5K GitHub stars and 19.4K forks on GitHub has more adoption than Pig with 583 GitHub stars and 449 GitHub forks.
According to the StackShare community, Apache Spark has a broader approval, being mentioned in 266 company stacks & 112 developers stacks; compared to Pig, which is listed in 9 company stacks and 4 developer stacks.
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What is Pig?
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