Cloudera Data Analyst Training

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Cloudera Data Analyst Training

BD-348

Apache Hive makes multi-structured data accessible to analysts, database administrators, and others without Java programming expertise. Apache Pig applies the fundamentals of familiar scripting languages to the Hadoop cluster. Cloudera Impala enables real-time interactive analysis of the data stored in Hadoop via a native SQL environment.
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. Cloudera presents the tools data professionals need to access, manipulate, transform, and analyze complex data sets using SQL and familiar scripting languages.

Course Objectives

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, ingestion, and processing with Hadoop tools
• 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

Course Topics
  • Hadoop Fundamentals
  • Introduction to Pig
  • Basic Data Analysis with Pig
  • Processing Complex Data with Pig
  • Multi-Dataset Operations with Pig
  • Pig Troubleshooting and Optimization
  • Introduction to Hive and Impala
  • Querying with Hive and Impala
  • Data Management
  • Data Storage and Performance
  • Relational Data Analysis with Hive and Impala
  • Working with Impala
  • Analyzing Text and Complex Data with Hive
  • Hive Optimization
  • Extending Hive
  • Choosing the Best Tool for the Job
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