Building Batch Data Analytics Solutions on AWS Course Overview

Building Batch Data Analytics Solutions on AWS Course Overview

The "Building Batch Data Analytics Solutions on AWS" course offers an in-depth exploration into constructing robust data analytics pipelines on the AWS platform. It equips learners with the skills to leverage AWS services for high-performance analytics, focusing on batch data processing using tools like Amazon EMR and Apache Spark.

Module 0 sets the stage by introducing key data analytics use cases and the crucial role of data pipelines for effective analytics. Module 1 dives into Amazon EMR, detailing its use in analytics solutions, cluster architecture, cost management, and includes an interactive demo for launching an EMR cluster. Module 2 looks at optimizing storage and data ingestion techniques for Amazon EMR.

Module 3 is dedicated to high-performance analytics using Apache Spark on Amazon EMR, including practical labs for hands-on experience. Module 4 continues with processing and analyzing batch data using Apache Hive and HBase on Amazon EMR.

In Module 5, learners discover serverless data processing and orchestrate workflows with AWS services like AWS Glue and AWS Step Functions. Module 6 covers the vital aspects of security, monitoring, and troubleshooting of EMR clusters, concluding with a design activity for a batch data analytics workflow. Finally, Module 7 provides insights into developing modern data architectures on AWS, broadening the scope for learners to design comprehensive analytics solutions. This course is a valuable resource for professionals seeking to enhance their batch data analytics capabilities on the AWS cloud.

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  • Live Online Training (Duration : 8 Hours)
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  • Including Official Coursebook

♱ Excluding VAT/GST

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Course Prerequisites

To ensure that participants are equipped to successfully undertake training in the "Building Batch Data Analytics Solutions on AWS" course, the following minimum prerequisites are recommended:

  • Basic understanding of big data technologies and their applications.
  • Familiarity with data warehousing and database systems.
  • Working knowledge of distributed systems and cloud computing concepts.
  • Prior experience with AWS services, particularly Amazon S3 and EC2.
  • Proficiency in at least one high-level programming language, such as Python, Scala, or Java.
  • Basic understanding of Linux operating systems and command-line interface usage.
  • Familiarity with SQL and data manipulation.
  • An introductory-level knowledge of data analytics and data pipelines.

These prerequisites are meant to provide a foundation that will help learners more effectively absorb the course content and participate in hands-on labs and demos. However, individuals with a strong desire to learn and a commitment to expanding their skills may find that they can successfully complete the course even if they do not meet all of the above criteria.

Target Audience for Building Batch Data Analytics Solutions on AWS

This course covers advanced data analytics on AWS, focusing on batch processing and data pipeline optimization for IT professionals.

  • Data Engineers
  • Data Scientists
  • Data Analysts
  • Solutions Architects
  • Cloud Computing Specialists
  • Business Intelligence Professionals
  • IT Managers overseeing data operations
  • Software Developers interested in data analytics
  • DevOps Engineers involved in data pipeline deployment
  • Technical Professionals looking to specialize in AWS analytics services
  • System Administrators aiming to expand their skill set into big data
  • AWS Certified Professionals seeking to deepen their expertise in data analytics services

Learning Objectives - What you will Learn in this Building Batch Data Analytics Solutions on AWS?

Introduction to Course Outcomes

This course empowers students with the skills necessary to build scalable batch data analytics solutions on AWS, leveraging tools such as Amazon EMR, Apache Spark, and Hive.

Learning Objectives and Outcomes

  • Understand data analytics use cases and how to implement data pipelines for effective analytics.
  • Learn the essentials of Amazon EMR, including cluster architecture and cost management strategies.
  • Gain hands-on experience in launching and managing Amazon EMR clusters through interactive demos.
  • Master storage optimization and data ingestion techniques specific to Amazon EMR.
  • Explore Apache Spark use cases on Amazon EMR, understand its benefits, and perform data transformations and analytics.
  • Acquire practical skills in connecting to an EMR cluster and executing Scala commands using the Spark shell.
  • Utilize notebooks with Amazon EMR for low-latency data analytics and gain proficiency with hands-on lab experience.
  • Process and analyze batch data efficiently using Amazon EMR with Apache Hive and HBase.
  • Discover serverless data processing options and learn to orchestrate data workflows using AWS Glue and Step Functions.
  • Enhance security measures, perform client-side encryption, and monitor EMR clusters, including troubleshooting and reviewing cluster history.

These objectives and outcomes are designed to provide a comprehensive understanding of building and optimizing batch data analytics workflows on AWS, preparing students to create robust, secure, and cost-effective data solutions.