Cloudera Data Analyst Training

Price
$3,195.00

Duration
4 Days

 

Delivery Methods
Virtual Instructor Led
Private Group

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Course Overview

Apache Hive makes transformation and analysis of complex, multi-structured data scalable in Hadoop. Apache Impala enables real-time interactive analysis of the data stored in Hadoop using a native SQL environment. Together, they make multi-structured data accessible to analysts, database administrators, and others without Java programming expertise.

Who Should Attend?

This course is designed for data analysts, business intelligence specialists, developers, system architects, and database administrators. Some knowledge of SQL is assumed, as is basic Linux command-line familiarity. Prior knowledge of Apache Hadoop is not required.

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Course Prerequisites

There are no prerequisites for this course.

Agenda

1 - Introduction

2 - Apache Hadoop Fundamentals

  • The Motivation for Hadoop
  • Hadoop Overview
  • Data Storage: HDFS
  • Distributed Data Processing: YARN, MapReduce, and Spark
  • Data Processing and Analysis: Hive and Impala
  • Database Integration: Sqoop
  • Other Hadoop Data Tools
  • Exercise Scenario Explanation

3 - Introduction to Apache Hive and Impala

  • What Is Hive?
  • What Is Impala?
  • Why Use Hive and Impala?
  • Schema and Data Storage
  • Comparing Hive and Impala to Traditional Databases
  • Use Cases

4 - Querying with Apache Hive and Impala

  • Databases and Tables
  • Basic Hive and Impala Query Language Syntax
  • Data Types
  • Using Hue to Execute Queries
  • Using Beeline (Hive's Shell)
  • Using the Impala Shell

5 - Common Operators and Built-In Functions

  • Operators
  • Scalar Functions
  • Aggregate Functions

6 - Data Management

  • Data Storage
  • Creating Databases and Tables
  • Loading Data
  • Altering Databases and Tables
  • Simplifying Queries with Views
  • Storing Query Results

7 - Data Storage and Performance

  • Partitioning Tables
  • Loading Data into Partitioned Tables
  • When to Use Partitioning
  • Choosing a File Format
  • Using Avro and Parquet File Formats

8 - Working with Multiple Datasets

  • UNION and Joins
  • Handling NULL Values in Joins
  • Advanced Joins

9 - Analytic Functions and Windowing

  • Using Analytic Functions
  • Other Analytic Functions
  • Sliding Windows

10 - Complex Data

  • Complex Data with Hive
  • Complex Data with Impala

11 - Analyzing Text

  • Using Regular Expressions with Hive and Impala
  • Processing Text Data with SerDes in Hive
  • Sentiment Analysis and n-grams in Hive

12 - Apache Hive Optimization

  • Understanding Query Performance
  • Cost-Based Optimization and Statistics
  • Bucketing
  • ORC File Optimizations

13 - Apache Impala Optimization

  • How Impala Executes Queries
  • Improving Impala Performance

14 - Extending Apache Hive and Impala

  • Custom SerDes and File Formats in Hive
  • Data Transformation with Custom Scripts in Hive
  • User-Defined Functions
  • Parameterized Queries

15 - Choosing the Best Tool for the Job

  • Comparing Hive, Impala, and Relational Databases
  • Which to Choose?

16 - Conclusion

  • Apache Kudu
  • What Is Kudu?
  • Kudu Tables
  • Using Impala with Kudu
 

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