Cloudera Data Analyst Training for Apache Hadoop Course Overview

Cloudera Data Analyst Training for Apache Hadoop Course Overview

The Cloudera Data Analyst Training for Apache Hadoop is a comprehensive course designed for analysts who want to leverage the power of Hadoop to work with big data. It provides hands-on experience with tools like Hive and Impala, key components of the Hadoop ecosystem. Learners will gain insights into Hadoop's motivation, its architecture, and how to perform Data Processing and analysis with various Hadoop tools.

By the end of the course, participants will be well-versed in querying, managing data, and optimizing performance within the Hadoop ecosystem. This knowledge is critical for earning the Cloudera Data Analyst Certification. The certification demonstrates proficiency in data analysis techniques and the use of Hadoop tools, making individuals stand out in their professional field. The Cloudera Data Analyst Training equips learners with the skills necessary to make data-driven decisions and to choose the right tool for any data analysis task.

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  • Live Online Training (Duration : 32 Hours)
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  • Live Online Training (Duration : 32 Hours)
  • Per Participant

♱ Excluding VAT/GST

Classroom Training price is on request

You can request classroom training in any city on any date by Requesting More Information

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Course Prerequisites

To ensure you gain the maximum benefit from the Cloudera Data Analyst Training for Apache Hadoop course, the following are the minimum required prerequisites:

  • Basic understanding of SQL: Familiarity with the SQL query language will help you grasp Hive and Impala query syntax more easily.
  • Basic knowledge of Linux command line: As Hadoop runs on Linux, being comfortable with Linux commands will be beneficial for interacting with the Hadoop ecosystem.
  • Fundamental understanding of databases: Knowing how traditional databases work will help you understand the motivations behind Hadoop and the use cases for Hive and Impala.
  • Analytical skills: The ability to think critically and analytically will assist you in understanding data processing and analysis techniques.
  • (Optional) Experience with traditional data warehousing concepts: While not mandatory, prior exposure to data warehousing can provide useful context for learning Hadoop's approach to data analysis.

While these are the minimum prerequisites, remember that the course is designed to guide you through each concept step-by-step, building your knowledge as you progress through the modules.

Target Audience for Cloudera Data Analyst Training for Apache Hadoop

Cloudera Data Analyst Training for Apache Hadoop equips participants with essential skills for big data analytics using Hadoop tools.

  • Data Analysts interested in big data and Hadoop
  • Business Intelligence Professionals seeking to understand Hadoop ecosystems
  • Database Administrators looking to expand into Hadoop-based systems
  • Data Engineers who require proficiency in Hive and Impala
  • IT Professionals aiming to specialize in big data analytics
  • Software Developers who need to understand Data Processing on Hadoop
  • System Architects planning to design big data solutions
  • Technical Managers overseeing data analytics projects
  • Data Scientists seeking to enhance their Data Processing capabilities
  • Hadoop Developers and Engineers looking to deepen their expertise in Hive and Impala

Learning Objectives - What you will Learn in this Cloudera Data Analyst Training for Apache Hadoop?

Introduction to the Course's Learning Outcomes and Concepts Covered

Gain a comprehensive understanding of Hadoop and its ecosystem, including HDFS, YARN, MapReduce, Spark, Hive, and Impala, to effectively store, process, manage, and analyze big data.

Learning Objectives and Outcomes

  • Understand the motivation behind using Apache Hadoop and its core components like HDFS for data storage and YARN for resource management.
  • Learn the fundamentals of distributed Data Processing with MapReduce and Spark, and how to analyze data using Pig, Hive, and Impala.
  • Gain proficiency in schema design, data storage, and query execution with Apache Hive and Impala, and understand their use cases and advantages over traditional databases.
  • Develop skills in writing, executing, and optimizing HiveQL and Impala queries to perform data analysis and manipulation tasks.
  • Master Data Management techniques including creating, loading, altering databases/tables, and simplifying queries with views in Hive and Impala.
  • Learn about data storage optimization through partitioning, choosing efficient file formats like Avro and Parquet, and understanding their impact on performance.
  • Acquire the ability to work with multiple datasets using UNIONs, joins, and handling NULL values in data analysis processes.
  • Understand and apply analytic functions and windowing in Hive and Impala to perform advanced data analysis.
  • Gain insights into text Data Processing, including the use of regular expressions and SerDes for sentiment analysis in Hive.
  • Optimize Apache Hive and Impala performance by understanding query execution plans, bucketing, and specific optimization techniques for each tool.
  • Extend the capabilities of Hive and Impala by integrating custom SerDes, file formats, scripting for data transformation, and user-defined functions.
  • Evaluate and choose the most appropriate tool between Hive, Impala, and traditional relational databases for various Data Processing tasks.