Cloudera Data Analyst Training for Apache Hadoop

Duration: 4 Days (32 Hours)

Cloudera Data Analyst Training for Apache Hadoop:

Welcome to the Cloudera Data Analyst Training for Apache Hadoop – a comprehensive program designed to empower individuals with expertise in analyzing extensive datasets using Apache Hadoop. Developed by Cloudera, this course covers fundamental concepts to real-time data exploration, focusing on tools like Apache Hadoop, Scala, and Crunch. You’ll learn about Hadoop’s core components, MapReduce architecture, data processing lifecycle, and cluster management. This course not only helps you analyze datasets using Hadoop but also equips you with industry best practices and proficiency in querying and manipulating vast datasets. Embark on this journey for an enriching learning experience and the aptitude to process and dissect large datasets through Apache Hadoop.

Intended Audience:

  • Aspiring Data Analysts
  • IT Professionals
  • Database Administrators
  • Business Analysts
  • Software Engineers
  • Data Enthusiasts
  • Graduates and Students
  • Career Transitioners

Learning Objectives of Active Directory Services with Windows Server:

  • Master the fundamentals of Apache Hadoop and its distributed file system (HDFS).
  • Acquire practical skills in utilizing Cloudera’s robust tools for data extraction, transformation, and loading within Hadoop.
  • Learn data querying through Hive and unstructured data processing with Pig on HDFS.
  • Develop proficiency in crafting and optimizing data storage using Cloudera tools like Sqoop, Flume, and more.
  • Become adept in Apache HBase and comprehending Hadoop Architectures.
  • Optimize MapReduce queries using advanced tuning techniques.
  • Grasp techniques for ingesting and reporting on big data.
  • Gain insight into data exploration through graph analysis.
  • Analyze data effectively using Impala and Solr.
  • Utilize Hue for reporting and data visualization.
  • Receive an introductory overview of Apache Spark.
  • Attain skills in storing and analyzing data using Apache Kafka.
Module 1: 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 Scenario Explanation
  • What Is Hive?
  • What Is Impala?
  • Why Use Hive and Impala?
  • Schema and Data Storage
  • Comparing Hive and Impala to Traditional Databases
  • Use Cases
  • 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 Storage
  • Creating Databases and Tables
  • Loading Data
  • Altering Databases and Tables
  • Simplifying Queries with Views
  • Storing Query Results
  • Partitioning Tables
  • Loading Data into Partitioned Tables
  • When to Use Partitioning
  • Choosing a File Format
  • Using Avro and Parquet File Formats
  • UNION and Joins
  • Handling NULL Values in Joins
  • Advanced Joins
  • Using Common Analytic Functions
  • Other Analytic Functions
  • Sliding Windows
  • Complex Data
  • Complex Data with Hive
  • Complex Data with Impala
  • Using Regular Expressions with Hive and Impala
  • Processing Text Data with SerDes in Hive
  • Sentiment Analysis and n-grams
  • Understanding Query Performance
  • Bucketing
  • Hive on Spark
  • How Impala Executes Queries
  • Improving Impala Performance
  • Custom SerDes and File Formats in Hive
  • Data Transformation with Custom Scripts in Hive
  • User-Defined Functions
  • Parameterized Queries
  • Comparing Hive, Impala, and Relational Databases

Course Prerequisites

  • Foundational familiarity with SQL and Linux
  • Proficiency in databases and data modeling concepts
  • Prior hands-on exposure to Big Data analytics tools
  • Experience with Apache Hadoop ecosystem including MapReduce, Apache Hive, Apache Pig, Apache Spark, and Apache Impala
  • Competence in scripting languages like Java, Python, and Scala
  • Capability to develop, debug, and optimize code in Hive, Pig, and Spark
  • Knowledge of data warehousing and comfort with SQL transformations

Discover the perfect fit for your learning journey

Choose Learning Modality

Live Online

  • Convenience
  • Cost-effective
  • Self-paced learning
  • Scalability


  • Interaction and collaboration
  • Networking opportunities
  • Real-time feedback
  • Personal attention


  • Familiar environment
  • Confidentiality
  • Team building
  • Immediate application

Training Exclusives

This course comes with following benefits:

  • Practice Labs.
  • Get Trained by Certified Trainers.
  • Access to the recordings of your class sessions for 90 days.
  • Digital courseware
  • Experience 24*7 learner support.

Got more questions? We’re all ears and ready to assist!

Request More Details

Please enable JavaScript in your browser to complete this form.

Subscribe to our Newsletter

Please enable JavaScript in your browser to complete this form.