The Hive data warehouse supports analytical processing, it generally processes long-running jobs which crunch a huge amount of data. By understanding what goes on behind the scenes in Hive, you can structure your Hive queries to be optimal and performant, thus making your data analysis very efficient. In this course, Writing Complex Analytical Queries with Hive, you’ll discover how to make design decisions and how to lay out data in your Hive tables. First, you’ll dive into partitioning and bucketing, which are ways to reduce the data a query has to process. You’ll cover how and when you use partitioning, bucketing, or both when you set up your tables. Next, you’ll be introduced to the joins operation, along with covering how to deal with large tables, and run and optimize map-only joins. Lastly, you’ll learn windowing functions, which allow you to write complex queries simply and easily with no intermediate tables. An important optimization with large datasets. By the end of this course, you’ll develop an understanding for the little details that makes writing complex queries easier and faster.
Author Name: Janani Ravi
Author Description:
Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing … more

Table of Contents

• Course Overview
  1min
• Using Hive for Analytical Queries
  21mins
• Partitioning Tables for Faster Queries
  42mins
• Bucketing Columns for Faster Joins
  38mins
• Optimizing Hive Joins
  47mins
• Windowing Functions
  31mins