Hive for Processing Big Data : From 0 to 1

About course

• 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. 

  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. 

   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 . 

Curriculam

• Section 1: You, Us & This Course
  	Lecture 1	You, Us & This Course	02:02
  Section 2: Introducing Hive
  	Lecture 2	Hive: An Open-Source Data Warehouse	12:59
  	Lecture 3	Hive and Hadoop	09:19
  	Lecture 4	Hive vs Traditional Relational DBMS	13:52
  	Lecture 5	HiveQL and SQL	07:20
  Section 3: Hadoop and Hive Install
  	Lecture 6	Hadoop Install Modes	08:32
  	Lecture 7	Setting up a Virtual Linux Instance - For Windows Users	13:50
  	Lecture 8	Hadoop Install Step 1 : Standalone Mode	09:33
  	Lecture 9	Hadoop Install Step 2 : Pseudo-Distributed Mode	14:25
  	Lecture 10	Hive install	12:05
  	Lecture 11	Code-Along: Getting started	06:24
  Section 4: Hadoop and HDFS Overview
  	Lecture 12	What is Hadoop?	07:25
  	Lecture 13	HDFS or the Hadoop Distributed File System	11:00
  Section 5: Hive Basics
  	Lecture 14	Primitive Datatypes	17:07
  	Lecture 15	Collections_Arrays_Maps	09:28
  	Lecture 16	Structs and Unions	05:57
  	Lecture 17	Create Table	13:15
  	Lecture 18	Insert Into Table	12:04
  	Lecture 19	Insert into Table 2	06:51
  	Lecture 20	Alter Table	07:22
  	Lecture 21	HDFS	09:25
  	Lecture 22	HDFS CLI - Interacting with HDFS	10:58
  	Lecture 23	Code-Along: Create Table	09:54
  	Lecture 24	Code-Along : Hive CLI	03:06
  Section 6: Built-in Functions
  	Lecture 25	Three types of Hive functions	06:45
  	Lecture 26	The Case-When statement, the Size function, the Cast function	10:09
  	Lecture 27	The Explode function	13:07
  	Lecture 28	Code-Along : Hive Built - in functions	04:28
  Section 7: Sub-Queries
  	Lecture 29	Quirky Sub-Queries	07:13
  	Lecture 30	More on subqueries: Exists and In	15:13
  	Lecture 31	Inserting via subqueries	05:23
  	Lecture 32	Code-Along : Use Subqueries to work with Collection Datatypes	05:56
  	Lecture 33	Views	12:18
  Section 8: Partitioning
  	Lecture 34	Indices	06:40
  	Lecture 35	Partitioning Introduced	06:36
  	Lecture 36	The Rationale for Partitioning	06:16
  	Lecture 37	How Tables are Partitioned	09:52
  	Lecture 38	Using Partitioned Tables	05:27
  	Lecture 39	Dynamic Partitioning: Inserting data into partitioned tables	12:44
  	Lecture 40	Code-Along : Partitioning	04:03
  Section 9: Bucketing
  	Lecture 41	Introducing Bucketing	11:56
  	Lecture 42	The Advantages of Bucketing	04:54
  	Lecture 43	How Tables are Bucketed	12:36
  	Lecture 44	Using Bucketed Tables	07:22
  	Lecture 45	Sampling	11:13
  Section 10: Windowing
  	Lecture 46	Windowing Introduced	12:59
  	Lecture 47	Windowing - A Simple Example: Cumulative Sum	09:39
  	Lecture 48	Windowing - A More Involved Example: Partitioning	11:54
  	Lecture 49	Windowing - Special Aggregation Functions	15:07
  Section 11: Understanding MapReduce
  	Lecture 50	The basic philosophy underlying MapReduce	08:49
  	Lecture 51	MapReduce - Visualized and Explained	09:03
  	Lecture 52	MapReduce - Digging a little deeper at every step	10:21
  Section 12: MapReduce logic for queries: Behind the scenes
  	Lecture 53	MapReduce Overview: Basic Select-From-Where	11:33
  	Lecture 54	MapReduce Overview: Group-By and Having	09:12
  	Lecture 55	MapReduce Overview: Joins	14:17
  Section 13: Join Optimizations in Hive
  	Lecture 56	Improving Join performance with tables of different sizes	13:11
  	Lecture 57	The Where clause in Joins	04:52
  	Lecture 58	The Left Semi Join	12:11
  	Lecture 59	Map Side Joins: The Inner Join	09:41
  	Lecture 60	Map Side Joins: The Left, Right and Full Outer Joins	11:36
  	Lecture 61	Map Side Joins: The Bucketed Map Join and the Sorted Merge Join	07:52
  Section 14: Custom Functions in Python
  	Lecture 62	Custom functions in Python	10:40
  	Lecture 63	Code-Along : Custom Function in Python	05:45
  Section 15: Custom functions in Java
  	Lecture 64	Introducing UDFs - you're not limited by what Hive offers	04:38
  	Lecture 65	The Simple UDF: The standard function for primitive types	07:03
  	Lecture 66	The Simple UDF: Java implementation for replacetext()	08:34
  	Lecture 67	Generic UDFs, the Object Inspector and DeferredObjects	13:50
  	Lecture 68	The Generic UDF: Java implementation for containsstring()	09:11
  	Lecture 69	The UDAF: Custom aggregate functions can get pretty complex	14:09
  	Lecture 70	The UDAF: Java implementation for max()	09:21
  	Lecture 71	The UDAF: Java implementation for Standard Deviation	10:47
  	Lecture 72	The Generic UDTF: Custom table generating functions	07:37
  	Lecture 73	The Generic UDTF: Java implementation for namesplit()	10:21
  Section 16: SQL Primer - Select Statemets
  	Lecture 74	Select Statements	11:46
  	Lecture 75	Select Statements 2	14:11
  	Lecture 76	Operator Functions	06:55
  Section 17: SQL Primer - Group By, Order By and Having
  	Lecture 77	Aggregation Operators Introduced	18:15
  	Lecture 78	The Group By Clause	17:19
  	Lecture 79	More Group By Examples	19:46
  	Lecture 80	Order By	16:15
  	Lecture 81	Having	19:52
  Section 18: SQL Primer - Joins
  	Lecture 82	Introduction to SQL Joins	09:53
  	Lecture 83	Cross Joins aka Cartesian Joins	17:02
  	Lecture 84	Inner Joins	19:52
  	Lecture 85	Left Outer Joins	15:31
  	Lecture 86	RIght, Full Outer Joins, Natural Joins, Self Joins	16:08

What you will get from this course?

• • 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
  • 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

Who should buy this course?

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