Cloudera Data Analyst Training for Apache Hadoop

Cloudera Data Analyst Training for Apache Hadoop is designed to equip data professionals with the knowledge and skills necessary to perform powerful data analysis and mining using the Hadoop ecosystem. This course will teach you how to leverage Cloudera’s suite of tools for big data processing, focusing on Apache Hadoop and its associated frameworks like Hive, Impala, and Pig. You’ll learn how to query, analyze, and visualize large-scale datasets to make data-driven decisions. Through practical hands-on experience, you will master the essential tools and techniques for performing complex data analysis in a Hadoop-based environment.

Schedule & Fee
Learning Objectives
Prerequisites
Target Audience
Course Modules
FAQs

Schedule	Learners	Course Fee (Incl. of all Taxes)	Register Your Interest
December 22^nd - 25^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 32 Hours) Guaranteed-to-Run		10% Off $1,600 $1,440 Fast Filling! Hurry Up.
December 27^th - 04^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 32 Hours)		10% Off $1,600 $1,440
January 05^th - 08^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 32 Hours)		20% Off $1,600 $1,280
January 10^th - 18^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 32 Hours)		20% Off $1,600 $1,280
January 12^th - 15^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 32 Hours)		20% Off $1,600 $1,280
January 19^th - 28^th 06:00 AM - 10:00 PM (CST) Live Virtual Classroom (Duration : 32 Hours)		20% Off $1,600 $1,280
January 26^th - 29^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 32 Hours) Guaranteed-to-Run		20% Off $1,600 $1,280

Course Prerequisites

Basic knowledge of databases and SQL
Familiarity with data analysis concepts
A fundamental understanding of big data and distributed computing principles
No prior experience with Hadoop is required, though familiarity with programming or scripting is helpful

Learning Objectives

By the end of this course, participants will be able to:

Navigate and manage Hadoop ecosystems and components in Cloudera
Analyze large datasets using Apache Hive, Impala, and Apache Pig
Write complex SQL queries and Pig scripts for data analysis tasks
Optimize performance for large-scale data processing
Visualize data analysis results and export them for business insights
Use advanced Cloudera tools and Hadoop components to perform deep data analysis

Target Audience

This course is designed for professionals who want to enhance their data analysis capabilities using the Hadoop ecosystem. The target audience includes:

Data Analysts
Data Scientists
Data Engineers
Business Analysts
IT Professionals working with big data
Professionals interested in transitioning into big data analytics

Course Modules

Introduction to Apache Hadoop and Cloudera
- Overview of Apache Hadoop and its role in big data analytics
- Key components of the Hadoop ecosystem and Cloudera’s distribution
- Understanding the architecture of Hadoop (HDFS, YARN, and MapReduce)
Working with Data in Cloudera
- Introduction to Cloudera Manager for cluster management
- Overview of file storage and data formats in HDFS
- Using Apache Hive and Impala for data query execution
Data Transformation with Apache Pig
- Introduction to Apache Pig and its role in data transformation
- Writing and running Pig scripts for ETL (Extract, Transform, Load)
- Using Pig Latin for data manipulation and analysis
Querying Data with Apache Hive and Impala
- Querying and analyzing data using Apache Hive
- Writing efficient HiveQL queries and understanding Hive architecture
- Using Apache Impala for fast, low-latency SQL queries
Data Analysis Techniques in Hadoop
- Analyzing structured, semi-structured, and unstructured data
- Using Hive and Impala to handle large-scale datasets for analytical queries
- Exploring Hadoop for batch and real-time data analytics
Data Visualization and Reporting
- Techniques for visualizing data analysis results using Cloudera tools
- Exporting data analysis results for use with BI (Business Intelligence) tools
- Integrating Hadoop with data visualization tools like Tableau and Qlik
Optimizing Data Analysis Performance
- Best practices for optimizing Hadoop queries
- Improving performance with indexing, partitioning, and file formats
- Optimizing query execution in Hive and Impala for faster results
Advanced Data Analysis with Hadoop Ecosystem
- Exploring advanced analytical techniques using Apache Spark and other tools
- Advanced data modeling and machine learning workflows with Cloudera tools
- Managing large-scale data pipelines for efficient analytics

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