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From 0 to 1 : Hive for Big Data Processing
$99.00
Connect the Dots Between SQL & Hive to Enhance Your Big Data Processing Skills
Mode Of Learning: Online
Access Duration: 365 days
Description
Hive is a Big Data processing tool that helps you leverage the power of distributed computing and Hadoop for analytical processing. Its interface is somewhat similar to SQL, but with some key differences. This course is an end-to-end guide to using Hive and connecting the dots to SQL. It’s perfect for both professional and aspiring data analysts and engineers alike. Don’t know SQL? No problem, there’s a primer included in this course!
COURSE OVERVIEW
What’s Inside
- Access 86 lectures & 15 hours of content 24/7
- Write complex analytical queries on data in Hive & uncover insights
- Leverage ideas of partitioning & bucketing to optimize queries in Hive
- Customize Hive w/ user defined functions in Java & Python
- Understand what goes on under the hood of Hive w/ HDFS & MapReduce
COURSE CONTENTS
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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)
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Hadoop and Hive Install
- Hadoop Install Modes (8:32)
- Setting up a Virtual Linux Instance – For Windows Users (13:50)
- Hadoop Install Step 1 : Standalone Mode (9:33)
- Hadoop Install Step 2 : Pseudo-Distributed Mode (14:25)
- 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)
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Hive Basics
- Primitive Datatypes (17:07)
- CollectionsArraysMaps (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)
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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)
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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)
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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)
TARGET AUDIENCE
There is no pre-requisite to take this training course. Anyone who is interested to build a career in Big Data Technologies can go for this training course. Knowledge of SQL and Java would be helpful
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