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Pig vs Azure Data Factory: What are the differences?
Pig: 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. ; Azure Data Factory: Create, Schedule, & Manage Data Pipelines. It is a service designed to allow developers to integrate disparate data sources. It is a platform somewhat like SSIS in the cloud to manage the data you have both on-prem and in the cloud.
Pig and Azure Data Factory can be categorized as "Big Data" tools.
Pig and Azure Data Factory are both open source tools. It seems that Pig with 585 GitHub stars and 448 forks on GitHub has more adoption than Azure Data Factory with 150 GitHub stars and 255 GitHub forks.
I have to collect different data from multiple sources and store them in a single cloud location. Then perform cleaning and transforming using PySpark, and push the end results to other applications like reporting tools, etc. What would be the best solution? I can only think of Azure Data Factory + Databricks. Are there any alternatives to #AWS services + Databricks?
Pros of Azure Data Factory
Pros of Pig
- Finer-grained control on parallelization2
- Proven at Petabyte scale1
- Open-source1
- Join optimizations for highly skewed data1