End-to-End Hive : HQL, Partitioning, Bucketing, UDFs, Windowing, Optimization, Map Joins, Indexes

What Will I Learn?


Write complex analytical queries on data in Hive and uncover insights
Leverage ideas of partitioning, bucketing to optimize queries in Hive
Customize hive with user defined functions in Java and Python
Understand what goes on under the hood of Hive with HDFS and MapReduce
 
Requirements


  • Hive requires knowledge of SQL. If you don’t know SQL, please head to the SQL primer at the end of the course first.
  • You’ll need to know Java if you are interested in the sections on custom user defined functions
  • No other prerequisites: The course covers everything you need to install Hive and run queries!
 
Description


Prerequisites: Hive requires knowledge of SQL. The course includes and SQL primer at the end. Please do that first if you don’t know SQL. You’ll need to know Java if you want to follow the sections on custom functions. 

Taught by a 4 person team including 2 Stanford-educated, ex-Googlers  and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with large-scale data. 

 Hive is like a new friend with an old face (SQL). This course is an end-to-end, practical guide to using Hive for Big Data processing. 

Let’s parse that 

A new friend with an old face: Hive helps you leverage the power of Distributed computing and Hadoop for Analytical processing. It’s interface is like an old friend : the very SQL like HiveQL. This course will fill in all the gaps between SQL and what you need to use Hive. 

End-to-End: The course is an end-to-end guide for using Hive:  whether you are analyst who wants to process data  or an Engineer who needs to build custom functionality or optimize performance – everything you’ll need is right here. New to SQL? No need to look elsewhere. The course  has a primer on all the basic SQL constructs, . 

Practical: Everything is taught using real-life examples, working queries and code . 

What’s Covered: 


Analytical Processing: Joins, Subqueries, Views, Table Generating Functions, Explode, Lateral View, Windowing and more

Tuning Hive for better functionality: Partitioning, Bucketing, Join Optimizations, Map Side Joins, Indexes, Writing custom User Defined functions in Java. UDF, UDAF, GenericUDF, GenericUDTF,  Custom functions in Python,  Implementation of MapReduce for Select, Group by and Join

For SQL Newbies: SQL In Great Depth 

 

Using discussion forums


Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We’re super small and self-funded with only 2-3 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!

 

Who is the target audience?


  • Yep! Analysts who want to write complex analytical queries on large scale data
  • Yep! Engineers who want to know more about managing Hive as their data warehousing solution

Course Curriculum

You, Us & This Course
Introducing Hive
Hadoop and Hive Install
Hadoop and HDFS Overview
Hive Basics
Built-in Functions
Sub-Queries
Partitioning
Bucketing
Windowing
Understanding MapReduce
MapReduce logic for queries: Behind the scenes
Join Optimizations in Hive
Custom Functions in Python
Custom functions in Java
SQL Primer - Select Statemets
SQL Primer - Group By, Order By and Having
SQL Primer - Joins
Appendix

Course Preview

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Reviews

Great samples and explanation, however, it would be even better if the code along samples would match the samples on its specific section.

Fidel Gruber

Its was awesome. Covered everything in detail.

Neha Tevathia

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