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Hive Tutorial: Process Big Data

Learn HQL, Partitioning, Bucketing, UDAFS, Windowing, Optimization, Map Side Joins, Indexes and learn how to write custom user defined functions in Java and Python in this comprehensive Hive tutorial

14h:56m
Lifetime access
35 learners
Course Overview

Hive is like a new ally with an old face (SQL). This course is a practical guide that covers everything right from the beginning to the end about how to use Hive for Big Data processing.

Let's parse that

A new ally with an old face: Hive assists you in harnessing the power of Distributed computing and Hadoop for Analytical processing. Its interface is similar to that of an old ally: HiveQL is very similar to SQL. This Hive tutorial will plug in all the gaps between SQL and what you need to know in order to use Hive.

Beginning to end: This course is the ultimate guide for using Hive. Whether you are an analyst who wants to process big data, or an engineer who wishes to build customized functionality or optimize performance, you will learn everything you need to accomplish these objectives right here. Are you new to SQL? Look no further. This Hive tutorial has a primer on all the basic SQL constructs.

A practical method of teaching: Every topic covered in this tutorial is taught using real-life examples, working queries and code.

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Course Objectives

By the end of this course you will be able to:

  • Write complex analytical queries in Hive for big data and unearth valuable insights
  • Harness ideas of partitioning and bucketing to optimize queries in Hive
  • Personalize Hive with user-defined functions in Java and Python
  • Figure out how Hive works with MapReduce and HDFS

Prerequisites and Target Audience

Prerequisites for the course

  • To subscribe to this course, knowledge of SQL is necessary. If you don't know SQL, the last 3 sections of the course (SQL primer) will teach you just that.
  • Knowledge of basics in Java is required to learn custom user defined functions taught in the course

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Course Plan
Certificate of completion

1. You, Us & This Course
1 video
You, Us & This Course 02:03
2. Introducing Hive
4 videos
Hive: An Open-Source Data Warehouse 12:59

Hive and Hadoop 09:19

Hive vs Traditional Relational DBMS 13:52

HiveQL and SQL 07:20
3. Hadoop and Hive Install
6 videos
Hadoop Install Step 1 : Standalone Mode 09:33

Hadoop Install Step 2 : Pseudo-Distributed Mode 14:25

Hive install 07:25

Code-Along: Getting started 06:24
4. Hadoop and HDFS Overview
2 videos
What is Hadoop? 07:25

HDFS or the Hadoop Distributed File System 11:00
5. Hive Basics
11 videos
Primitive Datatypes 17:07

Collections_Arrays_Maps 09:28

Structs and Unions 05:57

Create Table 13:15

Insert Into Table 12:05

Insert Into Table 2 06:51

Alter Table 07:22

HDFS 09:25

HDFS CLI - Interacting with HDFS 10:59

Code-Along: Create Table 09:54

Code-Along : Hive CLI 03:06
7. Sub-Queries
5 videos
Quirky Sub-Queries 07:13

More on subqueries: Exists and In 15:13

Inserting via subqueries 05:23

Code-Along : Use Subqueries to work with Collection Datatypes 05:57

Views 12:18
8. Partitioning
7 videos
Indices 06:40

Partitioning Introduced 06:36

The Rationale for Partitioning 06:16

How Tables are Partitioned 09:52

Using Partitioned Tables 05:27

Dynamic Partitioning: Inserting data into partitioned tables 12:44

Code-Along : Partitioning 04:03
9. Bucketing
5 videos
Introducing Bucketing 11:56

The Advantages of Bucketing 04:54

How Tables are Bucketed 12:36

Using Bucketed Tables 07:22

Sampling 11:13
10. Windowing
4 videos
Windowing Introduced 12:59

Windowing - A Simple Example: Cumulative Sum 09:39

Windowing - A More Involved Example: Partitioning 11:55

Windowing - Special Aggregation Functions 15:08
11. Understanding MapReduce
3 videos
The basic philosophy underlying MapReduce 08:49

MapReduce - Visualized and Explained 09:03

MapReduce - Digging a little deeper at every step 10:21
12. MapReduce logic for queries: Behind the scenes
3 videos
MapReduce Overview: Basic Select-From-Where 11:33

MapReduce Overview: Group-By and Having 09:12

MapReduce Overview: Joins 14:17
13. Join Optimizations in Hive
6 videos
Improving Join performance with tables of different sizes 13:12

The Where clause in Joins 04:52

The Left Semi Join 12:11

Map Side Joins: The Inner Join 09:41

Map Side Joins: The Left, Right and Full Outer Joins 11:36

Map Side Joins: The Bucketed Map Join and the Sorted Merge Join 07:52
14. Custom Functions in Python
2 videos
Custom functions in Python 10:40

Code-Along : Custom Function in Python 05:45
15. Custom functions in Java
10 videos
Introducing UDFs - you're not limited by what Hive offers 04:38

The Simple UDF: The standard function for primitive types 07:03

The Simple UDF: Java implementation for replacetext() 08:34

Generic UDFs, the Object Inspector and DeferredObjects 13:50

The Generic UDF: Java implementation for containsstring() 09:11

The UDAF: Custom aggregate functions can get pretty complex 14:09

The UDAF: Java implementation for max() 09:21

The UDAF: Java implementation for Standard Deviation 10:47

The Generic UDTF: Custom table generating functions 07:37

The Generic UDTF: Java implementation for namesplit() 10:21
16. SQL Primer - Select Statemets
3 videos
Select Statements 11:46

Select Statements 2 14:11

Operator Functions 06:55
17. SQL Primer - Group By, Order By and Having
5 videos
Aggregation Operators Introduced 18:16

The Group By Clause 17:20

More Group By Examples 19:47

Order By 16:15

Having 19:52
18. SQL Primer - Joins
5 videos
Introduction to SQL Joins 09:54

Cross Joins aka Cartesian Joins 17:02

Inner Joins 19:52

Left Outer Joins 15:31

RIght, Full Outer Joins, Natural Joins, Self Joins 16:08

Meet the Author


Loonycorn
4 Alumni of Stanford, IIM-A, IITs and Google, Microsoft, Flipkart

Loonycorn is a team of 4 people who graduated from reputed top universities. Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh have spent years (decades, actually) working in the Tech sector across the world.

  • Janani: Graduated from Stanford and has worked for 7 years at Google (New York, Singapore). She also worked at Flipkart and Microsoft.
  • Vitthal: Studied at Stanford; worked at Google (Singapore), Flipkart, Credit Suisse, and INSEAD.
  • Swetha: An IIM Ahmedabad and IIT Madras alumnus having experience of working in Flipkart.
  • Navdeep: An IIT Guwahati alumnus and Longtime Flipkart employee.
  • More from Loonycorn
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