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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! Upon completion of this course, and all courses included in the bundle, you'll also receive a certification of completion validating your new skills! This is especially useful for including in your portfolio or resume, so future employers can feel confident in your skill set.- 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
- Includes a certification of completion
Loonycorn is comprised of four individuals—Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh—who have honed their tech expertises at Google and Flipkart. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.
Details & Requirements
- Length of time users can access this course: lifetime
- Access options: web streaming, mobile streaming
- Certification of completion not included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: all levels, but knowledge of SQL and Java would be helpful
Terms
- Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.
Course Outline
- 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)
- 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)
- 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)
- 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)
access
lifetime
content
15 Hours