Prerequisites: Working with Pig requires some basic knowledge of the SQL query language, a brief understanding of the Hadoop eco-system and MapReduce
Taught by a team which includes 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with large-scale data processing jobs.
Pig is aptly named, it is omnivorous, will consume any data that you throw at it and bring home the bacon!
Let’s parse that
omnivorous: Pig works with unstructured data. It has many operations which are very SQL-like but Pig can perform these operations on data sets which have no fixed schema. Pig is great at wrestling data into a form which is clean and can be stored in a data warehouse for reporting and analysis.
bring home the bacon: Pig allows you to transform data in a way that makes is structured, predictable and useful, ready for consumption.
Pig Basics: Scalar and Complex data types (Bags, Maps, Tuples), basic transformations such as Filter, Foreach, Load, Dump, Store, Distinct, Limit, Order by and other built-in functions.
Advanced Data Transformations and Optimizations: The mind-bending Nested Foreach, Joins and their optimizations using “parallel”, “merge”, “replicated” and other keywords, Co-groups and Semi-joins, debugging using Explain and Illustrate commands
Real-world example: Clean up server logs using Pig
- Yep! Analysts who want to wrangle large, unstructured data into shape
- Yep! Engineers who want to parse and extract useful information from large datasets