Packt+ | Advance your knowledge in tech

You're reading from From 0 to 1: Hive for Processing Big Data End-to-End Hive: HQL, Partitioning, Bucketing, UDFs, Windowing, Optimization, Map Joins, Indexes

Product type Video

Published in Dec 2017

Publisher Packt

ISBN-13 9781788995054

Length 15hrs 16mins

Edition 1st Edition

Languages

Processing

Tools

Hive

Concepts

Big Data

Author (1):

Loonycorn Ravi

View More author details

Table of Contents (19) Chapters

1. You, Us & This Course FREE CHAPTER

You, Us & This Course

2. Introducing Hive

Hive: An Open-Source Data Warehouse
Hive and Hadoop
Hive vs Traditional Relational DBMS
HiveQL and SQL

3. Hadoop and Hive Install

Hadoop Install Modes
Hadoop Install Step 1: Standalone Mode
Hadoop Install Step 2: Pseudo-Distributed Mode
Hive install
Code-Along: Getting started

4. Hadoop and HDFS Overview

What is Hadoop?
HDFS or the Hadoop Distributed File System

5. Hive Basics

Primitive Datatypes
Collections_Arrays_Maps
Structs and Unions
Create Table
Insert Into Table
Insert into Table 2
Alter Table
HDFS
HDFS CLI - Interacting with HDFS
Code-Along: Create Table
Code-Along: Hive CLI

6. Built-in Functions

Three types of Hive functions
The Case-When statement, the Size function, the Cast function
The Explode function
Code-Along: Hive Built - in functions

7. Sub-Queries

Quirky Sub-Queries
More on subqueries: Exists and In
Inserting via subqueries
Code-Along: Use Subqueries to work with Collection Datatypes
Views

8. Partitioning

Indices
Partitioning Introduced
The Rationale for Partitioning
How Tables are partitioned
Using Partitioned Tables
Dynamic Partitioning: Inserting data into partitioned tables
Code-Along: Partitioning

9. Bucketing

Introducing Bucketing
The Advantages of Bucketing
How Tables are bucketed
Using Bucketed Tables
Sampling

10. Windowing

Windowing Introduced
Windowing - A Simple Example: Cumulative Sum
Windowing - A More Involved Example: Partitioning
Windowing - Special Aggregation Functions

11. Understanding MapReduce

The basic philosophy underlying MapReduce
MapReduce - Visualized and Explained
MapReduce - Digging a little deeper at every step

12. MapReduce logic for queries: Behind the scenes

MapReduce Overview: Basic Select-From-Where
MapReduce Overview: Group-By and Having
MapReduce Overview: Joins

13. Join Optimizations in Hive

Improving Join performance with tables of different sizes
The Where clause in Joins
The Left Semi Join
Map Side Joins: The Inner Join
Map Side Joins: The Left, Right and Full Outer Joins
Map Side Joins: The Bucketed Map Join and the Sorted Merge Join

14. Custom Functions in Python

Custom functions in Python
Code-Along: Custom Function in Python

15. Custom functions in Java

Introducing UDFs - you're not limited by what Hive offer
The Simple UDF: The standard function for primitive types
The Simple UDF: Java implementation for replacetext()
Generic UDFs, the Object Inspector and DeferredObjects
The Generic UDF: Java implementation for containsstring()
The UDAF: Custom aggregate functions can get pretty complex
The UDAF: Java implementation for max()
The UDAF: Java implementation for Standard Deviation
The Generic UDTF: Custom table generating functions
The Generic UDTF: Java implementation for namesplit()

16. SQL Primer - Select Statements

Select Statements
Select Statements 2
Operator Functions

17. SQL Primer - Group By, Order by and Having

Aggregation Operators Introduced
The Group by Clause
More Group by Examples
Order by
Having

18. SQL Primer – Joins

Introduction to SQL Joins
Cross Joins and Cartesian Joins
Inner Joins
Left Outer Joins
Right, Full Outer Joins, Natural Joins, Self Joins

19. Appendix

[For Linux/Mac OS Shell Newbies] Path and other Environment Variables

You're reading from From 0 to 1: Hive for Processing Big Data End-to-End Hive: HQL, Partitioning, Bucketing, UDFs, Windowing, Optimization, Map Joins, Indexes

Table of Contents (19) Chapters

Authors (1)

Personalised recommendations for you