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Cloudera Data Analyst Training for Apache Hadoop

Length 4 days

Course overview

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Why study this course

This four-day data analyst course is for anyone who wants to access, manipulate, transform, and analyse massive data sets in the Hadoop cluster using SQL and familiar scripting languages. This is the core curriculum in the data analyst learning path.

Cloudera’s Data Analyst Training course focuses on 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 analyse complex data sets using SQL and familiar scripting languages.

Apache Hive makes transformation and analysis of complex, multi-structured data scalable in Cloudera environments. 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.

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What you’ll learn

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

How the open source ecosystem of big data tools addresses challenges not met by traditional RDBMSs
Using Apache Hive and Apache Impala to provide SQL access to data
Hive and Impala syntax and data formats, including functions and subqueries
Create, modify, and delete tables, views, and databases; load data; and store results of queries
Create and use partitions and different file formats
Combining two or more datasets using JOIN or UNION, as appropriate
What analytic and windowing functions are, and how to use them
Store and query complex or nested data structures
Process and analyse semi-structured and unstructured data
Techniques for optimising Hive and Impala queries
Extending the capabilities of Hive and Impala using parameters, custom file formats and SerDes, and external scripts
How to determine whether Hive, Impala, an RDBMS, or a mix of these is best for a given task

Cloudera at Lumify Work

Cloudera provides a scalable, flexible, integrated platform that makes it easy to manage rapidly increasing volumes and varieties of data in your enterprise. Cloudera products and solutions enable you to deploy and manage Apache Hadoop and related projects, manipulate and analyse your data, and keep that data secure and protected.

Stay ahead of the technology curve

Don’t let your tech outpace the skills of your people

Quality Instructors and Content

Expert instructors with real world experience and the latest vendor-approved in-depth course content.

Partner-Preferred Supplier

Chosen and awarded by the world's leading vendors as preferred training partner.

Ahead of the Technology Curve

No matter your chosen technologies or platforms, we can help you stay one step ahead.

Who is the course for?

This course is designed for:

data analysts
business intelligence specialists
developers
system architects
database administrators

Course subjects

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

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

Common Operators and Built-In Functions

Operators
Scalar Functions
Aggregate Functions

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

Working with Multiple Datasets

UNION and Joins
Handling NULL Values in Joins
Advanced Joins

Analytic Functions and Windowing

Using Common Analytic Functions
Other Analytic Functions
Sliding Windows

Complex Data

Complex Data with Hive
Complex Data with Impala

Analysing Text

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

Apache Hive Optimisation

Understanding Query Performance
Bucketing
Hive on Spark

Apache Impala Optimisation

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
Parameterised Queries

Choosing the Best Tool for the Job

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

Prerequisites

Some knowledge of SQL is assumed, as is basic Linux command-line familiarity.Prior knowledge of Apache Hadoop is not required.

Terms & Conditions

The supply of this course by Lumify Work is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.

Request Course Information

By submitting an enquiry, you agree to our privacy policy and receiving email and other forms of communication from us. You can opt-out at any time.