Hive is a data warehouse that runs on top of the Hadoop distributed computing framework. It works on huge datasets, so this course is useful for understanding its features so you can write efficient, fast, and optimal queries.
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.
What is Hive used for?
Hive is a data warehouse, which works on huge datasets, which means any query that you run on Hive is likely to be slow and long running without the tips and tricks in this course.
What will I learn in this course?
This course helps you make design decisions on how to layout data in your Hive tables, partitioning and bucketing are ways to reduce the data your query has to process, understand how and when you would use partitioning, bucketing or both.
What prerequisites do I need?
This course assumes that you have some familiarity with Hive and writing queries for it.
What software is required for this course?
You should have Hive v2 which runs on top of Hadoop 2, and have the Beeline command interface to connect to Hive locally.
A problem solver at heart, 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.
Course Overview Hi. My name is Janani Ravi, and welcome to this course on Writing Complex Analytical Queries in Hive. I'll introduce myself first. I have a master's degree in Electrical Engineering from Stanford, and I have worked with companies such as Microsoft, Google, and Flipkart. At Google I was one of the first engineers working on real time collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own startup, Loony Corn, a studio for high quality video content. Hive is a data warehouse that supports analytical processing. Analytical processing involves huge datasets summarizing and extracting insights from this dataset, and calculating trends. This basically means that Hive scripts tend to be very long running jobs, which require a lot of resources, but there is a lot you can do to make your queries run faster on Hive, and that's where this course comes in. This course helps you make design decisions on how to layout data in your Hive tables, partitioning and bucketing are ways to reduce the data your query has to process, understand how and when you would use partitioning, bucketing or both. Joining information from two or more tables is a very common operation, but it's often slow and inefficient in Hive. Learn how Hive deals with joins under the hood and how you can tweak your queries to have joins run faster. Lastly, we cover windowing functions. They allow you to write complex queries simply and easily with no intermediate tables. This course helps you understand the little details of Hive that makes writing complex queries easier and faster.