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From 0 to 1: Hive for Processing Big Data
You, Us & This Course
You, Us & This Course (2:02)
Introducing Hive
Hive: An Open-Source Data Warehouse (12:59)
Hive and Hadoop (9:19)
Hive vs Traditional Relational DBMS (13:52)
HiveQL and SQL (7:20)
Hadoop and Hive Install
Hadoop Install Modes (8:32)
Hadoop Install Step 1 : Standalone Mode (15:46)
Hadoop Install Step 2 : Pseudo-Distributed Mode (11:44)
Hive install (12:05)
Code-Along: Getting started (6:24)
Hadoop and HDFS Overview
What is Hadoop? (7:25)
HDFS or the Hadoop Distributed File System (11:01)
Hive Basics
Primitive Datatypes (17:07)
Collections_Arrays_Maps (9:28)
Structs and Unions (5:57)
Create Table (13:15)
Insert Into Table (12:05)
Insert into Table 2 (6:51)
Alter Table (7:22)
HDFS (9:25)
HDFS CLI - Interacting with HDFS (10:59)
Code-Along: Create Table (9:54)
Code-Along : Hive CLI (3:06)
Built-in Functions
Three types of Hive functions (6:45)
The Case-When statement, the Size function, the Cast function (10:09)
The Explode function (13:07)
Code-Along : Hive Built - in functions (4:28)
Sub-Queries
Quirky Sub-Queries (7:13)
More on subqueries: Exists and In (15:13)
Inserting via subqueries (5:23)
Code-Along : Use Subqueries to work with Collection Datatypes (5:57)
Views (12:18)
Partitioning
Indices (6:40)
Partitioning Introduced (6:36)
The Rationale for Partitioning (6:16)
How Tables are Partitioned (9:52)
Using Partitioned Tables (5:27)
Dynamic Partitioning: Inserting data into partitioned tables (12:44)
Code-Along : Partitioning (4:03)
Bucketing
Introducing Bucketing (11:56)
The Advantages of Bucketing (4:54)
How Tables are Bucketed
Using Bucketed Tables (7:22)
Sampling (11:13)
Windowing
Windowing Introduced (12:59)
Windowing - A Simple Example: Cumulative Sum (9:39)
Windowing - A More Involved Example: Partitioning (11:55)
Windowing - Special Aggregation Functions (15:08)
Understanding MapReduce
The basic philosophy underlying MapReduce (8:49)
MapReduce - Visualized and Explained (9:03)
MapReduce - Digging a little deeper at every step (10:21)
MapReduce logic for queries: Behind the scenes
MapReduce Overview: Basic Select-From-Where (11:33)
MapReduce Overview: Group-By and Having (9:12)
MapReduce Overview: Joins (14:17)
Join Optimizations in Hive
Improving Join performance with tables of different sizes (13:12)
The Where clause in Joins (4:52)
The Left Semi Join (12:11)
Map Side Joins: The Inner Join (9:41)
Map Side Joins: The Left, Right and Full Outer Joins (11:36)
Map Side Joins: The Bucketed Map Join and the Sorted Merge Join (7:52)
Custom Functions in Python
Custom functions in Python (10:40)
Code-Along : Custom Function in Python (5:45)
Custom functions in Java
Introducing UDFs - you're not limited by what Hive offers (4:38)
The Simple UDF: The standard function for primitive types (7:03)
The Simple UDF: Java implementation for replacetext() (8:34)
Generic UDFs, the Object Inspector and DeferredObjects (13:50)
The Generic UDF: Java implementation for containsstring() (9:11)
The UDAF: Custom aggregate functions can get pretty complex (14:09)
The UDAF: Java implementation for max() (9:21)
The UDAF: Java implementation for Standard Deviation (10:47)
The Generic UDTF: Custom table generating functions (7:38)
The Generic UDTF: Java implementation for namesplit() (10:21)
SQL Primer - Select Statemets
Select Statements (11:46)
Select Statements 2 (14:11)
Operator Functions (6:55)
SQL Primer - Group By, Order By and Having
Aggregation Operators Introduced (18:15)
The Group By Clause (17:19)
More Group By Examples (19:46)
Order By (16:15)
Having (19:52)
SQL Primer - Joins
Introduction to SQL Joins (9:54)
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)
Appendix
[For Linux/Mac OS Shell Newbies] Path and other Environment Variables (8:25)
Setting up a Virtual Linux Instance - For Windows Users (15:58)
The UDAF: Java implementation for Standard Deviation
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