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Building Data Analytics Solutions Using Amazon Redshift

Last Updated: 08-03-2025

The Building Data Analytics Solutions Using Amazon Redshift course is designed for professionals who want to develop, deploy, and manage data analytics solutions using Amazon Redshift, AWS’s powerful data warehousing service. In this comprehensive course, you'll learn how to create scalable, high-performance data analytics architectures that provide real-time insights. You'll gain hands-on experience in loading, querying, and optimizing large datasets, as well as integrating Redshift with other AWS analytics tools like S3, Glue, and Athena.

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September 22nd
09:00 AM - 05:00 PM (IST)
Live Virtual Classroom (Duration : 8 Hours)
Guaranteed-to-Run
40% Off
₹27,789
₹16,673
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October 06th
09:00 AM - 05:00 PM (IST)
Live Virtual Classroom (Duration : 8 Hours)
50% Off
₹27,789
₹13,894
October 13th
09:00 AM - 05:00 PM (IST)
Live Virtual Classroom (Duration : 8 Hours)
50% Off
₹27,789
₹13,894
October 20th - 21st
06:00 PM - 10:00 PM (IST)
Live Virtual Classroom (Duration : 8 Hours)
50% Off
₹27,789
₹13,894
October 27th
09:00 AM - 05:00 PM (IST)
Live Virtual Classroom (Duration : 8 Hours)
Guaranteed-to-Run
50% Off
₹27,789
₹13,894
November 03rd
09:00 AM - 05:00 PM (IST)
Live Virtual Classroom (Duration : 8 Hours)
60% Off
₹27,789
₹11,116
November 10th
09:00 AM - 05:00 PM (IST)
Live Virtual Classroom (Duration : 8 Hours)
60% Off
₹27,789
₹11,116

Course Prerequisites

  • Familiarity with basic cloud computing concepts.
  • Basic knowledge of data warehousing, SQL, and relational databases.
  • Recommended: Experience with AWS services like S3, EC2, and IAM is beneficial, but not mandatory.

Learning Objectives

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

  1. Design and deploy scalable data analytics solutions using Amazon Redshift.
  2. Optimize Redshift clusters for performance and cost efficiency, including distribution keys and compression techniques.
  3. Implement data loading techniques using AWS Glue and Redshift Spectrum for querying data directly from S3.
  4. Integrate Redshift with AWS analytics tools like Amazon QuickSight and Athena for enhanced business intelligence capabilities.
  5. Use Redshift for complex data analysis and BI reporting at scale.
  6. Apply best practices for security, monitoring, and cost optimization in Redshift environments.
  7. Troubleshoot common Redshift performance issues and implement tuning strategies.
  8. Prepare to work with large datasets and support real-time analytics in a cloud-native environment.

Target Audience

This course is ideal for:

  • Data engineers and architects looking to build efficient, scalable analytics solutions on AWS.
  • Business analysts, data scientists, and BI professionals seeking to enhance their data warehousing skills with Amazon Redshift.
  • IT professionals interested in optimizing data storage and analytics performance using AWS tools.
  • Developers looking to integrate data analytics capabilities into cloud-native applications.

Course Modules

  • Introduction to Data Analytics Architectures:

    • Overview of data warehouses, data lakes, and modern data architectures.
    • Benefits and use cases of each architecture.

  • Designing Data Warehouse Analytics Solutions:

    • Best practices for designing scalable and efficient data warehouses.
    • Implementing analytics solutions using Amazon Redshift.

  • Optimizing Data Storage:

    • Techniques for data compression and storage optimization.
    • Managing storage costs and performance.

  • Data Ingestion and Transformation:

    • Methods for ingesting and transforming data into Amazon Redshift.
    • Utilizing AWS services for ETL (Extract, Transform, Load) processes.

  • Infrastructure and Performance Management:

    • Selecting appropriate instance types and configuring clusters.
    • Implementing auto-scaling and designing network topology.

  • Security and Cost Management:

    • Applying security best practices for data protection.
    • Strategies for effective cost management within Amazon Redshift.

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