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Overview

Cloudera University’s Data Analyst Training course focuses on Apache Pig, Apache Hive, and Apache Impala. You will learn how to apply traditional data analytics and business intelligence skills to big data. Cloudera presents the tools data professionals need to access, manipulate, transform, and analyze complex data sets using SQL and familiar scripting languages.

Hands-On Hadoop

Through instructor-led discussion and interactive, hands-on exercises, participants will navigate the Hadoop ecosystem, learning topics such as:

  • 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

Audience & Prerequisites

This course is designed for data analysts, business intelligence specialists, developers, system architects, and database administrators. Knowledge of SQL is assumed, as is basic Linux command-line familiarity. Knowledge of at least one scripting language (e.g., Bash scripting, Perl, Python, Ruby) would be helpful but is not essential. Prior knowledge of Apache Hadoop is not required.

Book the course

How would you like to train?

Advance your ecosystem expertise

Apache Pig applies the fundamentals of familiar scripting languages to the Hadoop cluster. Apache Hive makes transformation and analysis of complex, multi-structured data scalable in Hadoop. Cloudera Impala enables real-time interactive analysis of the data stored in Hadoop via a native SQL environment. Together, Pig, Hive, and Impala make multi-structured data accessible to analysts, database administrators, and others without Java programming expertise.

Course Contents

Introduction

Apache 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
  • Database Integration: Sqoop
  • Other Hadoop Data Tools
  • Exercise Scenarios

Introduction to Apache Pig

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

Basic Data Analysis with Apache Pig

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

Processing Complex Data with Apache Pig

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

Multi-Dataset Operations with Apache Pig

  • Techniques for Combining Datasets
  • Joining Datasets in Pig
  • Set Operations
  • Splitting Datasets

 

Apache Pig Troubleshooting and 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 Pig Jobs

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

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

Apache Hive and Impala Data Management

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

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

Relational Data Analysis with Apache Hive and Impala

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

Complex Data with Apache Hive and Impala

  • Complex Data with Hive
  • Complex Data with Impala

Analyzing Text with Apache Hive and Impala

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

Apache Hive Optimization

  • Understanding Query Performance
  • Bucketing
  • Indexing Data
  • Hive on Spark

Apache Impala Optimization

  • How Impala Executes Queries
  • Improving Impala Performance

Extending Apache Hive and Impala

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

Choosing the Best Tool for the Job

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

Conclusion

The professionalism and expansive technical knowledge demonstrated by our classroom instructor were incredible. The quality of Cloudera training was on par with a university.

General Dynamics

Learn more

Data Analyst Certification

Data Analyst Training is a useful precursor to the Cloudera Certified Data Analyst (CCA Data Analyst) exam. Certification is a great differentiator; it helps establish you as a leader in the field, providing employers and customers with tangible evidence of your skills and expertise.

Advance your career

Big data analysts are among the world's most in-demand and highly-compensated technical roles. Check out some of the job opportunities currently listed that match the professional profile, many of which seek experience with Impala, Hive, and Pig.

Private training

We also provide private training at your site, at your pace, and tailored to your needs.

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