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Cloudera Data Analyst Training: Using Pig, Hive, and Impala with Hadoop

Course Overview

Cloudera University’s four-day data analyst training course focusing on Apache Pig and Hive and Cloudera Impala will teach you to apply traditional data analytics and business intelligence skills to big data.

Who Should Attend

This course is designed for data analysts, business intelligence specialists, developers, system architects, and database administrators.

Course Objectives

Skills gained in this training include: The features that Pig, Hive, and Impala offer for data acquisition, storage, and analysis The fundamentals of Apache Hadoop and data ETL (extract, transform, load), ingestion, and processing with Hadoop How Pig, Hive, and Impala improve productivity for typical analysis tasks Joining diverse datasets to gain valuable business insight Performing real-time, complex queries on datasets

Course Outline

1 - Hadoop Fundamentals

  • The Motivation for Hadoop
  • Hadoop Overview
  • Data Storage: HDFS
  • Distributed Data Processing: YARN, MapReduce, and Spark
  • Data Processing and Analysis: Pig, Hive, and Impala
  • Data Integration: Sqoop
  • Other Hadoop Data Tools
  • Exercise Scenarios Explanation3fkeyword%3d

2 - Introduction to Pig

  • What Is Pig?
  • Pig’s Features
  • Pig Use Cases
  • Interacting with Pig

3 - Basic Data Analysis with Pig

  • Pig Latin Syntax
  • Loading Data
  • Simple Data Types
  • Field Definitions
  • Data Output
  • Viewing the Schema
  • Filtering and Sorting Data
  • Commonly-Used Functions

4 - Processing Complex Data with Pig

  • Storage Formats
  • Complex/Nested Data Types
  • Grouping
  • Built-In Functions for Complex Data
  • Iterating Grouped Data

5 - Multi-Dataset Operations with Pig

  • Techniques for Combining Data Sets
  • Joining Data Sets in Pig
  • Set Operations
  • Splitting Data Sets

6 - Pig Troubleshoot & Optimization

  • Troubleshooting Pig
  • Logging
  • Using Hadoop’s Web UI
  • Data Sampling and Debugging
  • Performance Overview
  • Understanding the Execution Plan
  • Tips for Improving the Performance of Your Pig Jobs

7 - Introduction to Hive & Impala

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

8 - Querying with Hive & Impala

  • Databases and Tables
  • Basic Hive and Impala Query Language Syntax
  • Data Types
  • Differences Between Hive and Impala Query Syntax
  • Using Hue to Execute Queries
  • Using the Impala Shell

9 - Data Management

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

10 - Data Storage & Performance

  • Partitioning Tables
  • Choosing a File Format
  • Managing Metadata
  • Controlling Access to Data

11 - Relational Data Analysis with Hive & Impala

  • Joining Datasets
  • Common Built-In Functions
  • Aggregation and Windowing

12 - Working with Impala

  • How Impala Executes Queries
  • Extending Impala with User-Defined Functions
  • Improving Impala Performance

13 - Analyzing Text and Complex Data with Hive

  • Complex Values in Hive
  • Using Regular Expressions in Hive
  • Sentiment Analysis and N-Grams
  • Conclusion3fkeyword%3d

14 - Hive Optimization

  • Understanding Query Performance
  • Controlling Job Execution Plan
  • Bucketing
  • Indexing Data

15 - Extending Hive

  • SerDes
  • Data Transformation with Custom Scripts
  • User-Defined Functions
  • Parameterized Queries

16 - Choosing the Best Tool for the Job

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

Enroll Today

This is a 4-day class

Price: $3,195.00
Payment Options

ILT Instructor‑Led Training


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Class times are listed Eastern time. This class is available for Private Group Training

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