Hadoop 3 with Hive 3


Hive is de-facto standard the SQL interface into Big Data. Hive has moved on to Hive 3. Today, it offers ACID tables, storage requirements reduction by the factor of 2 with erasure coding, HBase integration with Phoenix, and much more. However, in order to achieve efficiency, one must be familiar with the best practices of the HQL language, compare different tools for looking at your data, whether it be Hive, Phoenix HBase, or plan Excel.
This course will explain the capabilities of Hive, HQL dialects, and best practices.

What you will learn

  • Hive 3 new features
  • HBase with Phoenix


Two to three days


Business analysts, Software developers, Managers


  • • Exposure to SQL
    • Be able to navigate Linux command line

Lab environment

  • Working environment provided in the browser
  • Zero Install: There is no need to install software on students’ machines

Detailed Course Outline

Why Hadoop?

    • The motivation for Hadoop
    • Use cases and case studies about Hadoop

The Hadoop platform

    • MapReduce, HDFS, YARN
    • New in Hadoop 3
      ∗ Erasure Coding vs 3x replication

Hive Basics

    • Defining Hive Tables
    • SQL Queries over Structured Data
    • Filtering / SearchService Level Objectives
    • Aggregations / Ordering
    • Partitions
    • Joins
    • Text Analytics (Semi-Structured Data)

New in Hive 3

    • ACID tables
    • Hive Query Language (HQL)
      ∗ How to run a good query?
      ∗ How to troubleshoot queries?


    • Basics
    • HBase tables – design and use
    •  Phoenix driver for HBase tables


    • Tool
    • Architecture
    • Use


    • Overview
    • Spark SQL

The big picture

    • How Hadoop fits into your architecture
    •  Hive vs HBase with Phoenix vs Excel