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Building Modern Data Analytics Solutions on AWS

Last Updated: 08-03-2025

The Building Modern Data Analytics Solutions on AWS course is designed for professionals who want to create end-to-end data analytics architectures using the full range of AWS cloud services. In this course, you’ll learn how to design and implement modern data architectures that handle structured and unstructured data, perform advanced analytics, and integrate with machine learning workflows. Using AWS services like Amazon Redshift, Amazon S3, AWS Glue, and AWS Lambda, you'll develop the skills necessary to create scalable, real-time data pipelines, build data lakes, and optimize analytics for business insights.

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December 22nd - 25th
09:00 AM - 05:00 PM (CST)
Live Virtual Classroom (Duration : 32 Hours)
Guaranteed-to-Run
10% Off
$1,280
$1,152
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December 27th - 04th
09:00 AM - 05:00 PM (CST)
Live Virtual Classroom (Duration : 32 Hours)
10% Off
$1,280
$1,152
January 05th - 08th
09:00 AM - 05:00 PM (CST)
Live Virtual Classroom (Duration : 32 Hours)
20% Off
$1,280
$1,024
January 10th - 18th
09:00 AM - 05:00 PM (CST)
Live Virtual Classroom (Duration : 32 Hours)
20% Off
$1,280
$1,024
January 12th - 15th
09:00 AM - 05:00 PM (CST)
Live Virtual Classroom (Duration : 32 Hours)
20% Off
$1,280
$1,024
January 19th - 28th
06:00 AM - 10:00 PM (CST)
Live Virtual Classroom (Duration : 32 Hours)
20% Off
$1,280
$1,024
January 26th - 29th
09:00 AM - 05:00 PM (CST)
Live Virtual Classroom (Duration : 32 Hours)
Guaranteed-to-Run
20% Off
$1,280
$1,024

Course Prerequisites

  • Basic understanding of cloud computing concepts and AWS services.
  • Familiarity with relational databases, SQL, and data processing concepts.
  • Recommended: Experience with data engineering or data science concepts, and knowledge of tools like Python or Spark.

Learning Objectives

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

  1. Design and implement a modern data analytics architecture using AWS services such as Amazon Redshift, AWS Glue, and Amazon S3.
  2. Build and manage data lakes on AWS, integrating structured and unstructured data for advanced analytics.
  3. Set up ETL (Extract, Transform, Load) processes using AWS Glue for efficient data processing.
  4. Implement scalable data pipelines for real-time and batch processing using AWS Lambda and Amazon Kinesis.
  5. Optimize storage and query performance in data warehouses with Amazon Redshift and Amazon Aurora.
  6. Use Amazon Athena and AWS Glue to analyze large datasets stored in S3 without the need for a data warehouse.
  7. Apply machine learning capabilities to data analytics workflows using AWS SageMaker and AWS ML services.
  8. Implement best practices for security, data governance, and compliance in data analytics solutions.
  9. Optimize costs for data storage, processing, and analytics in the cloud.

Target Audience

This course is ideal for:

  • Data engineers, architects, and analysts responsible for building modern data analytics platforms on AWS.
  • IT professionals and system architects looking to leverage AWS for advanced data processing and analytics.
  • Business intelligence professionals seeking to implement end-to-end data analytics solutions in the cloud.
  • Developers and cloud practitioners who want to understand how to integrate big data analytics with AWS tools.

Course Modules

  • Building Data Lakes on AWS:

    • Introduction to Data Lakes: Understand the concept and benefits of data lakes.
    • Data Ingestion, Cataloging, and Preparation: Learn methods to ingest, catalog, and prepare data for analytics.
    • Data Processing and Analytics: Explore techniques for processing and analyzing data within a data lake.
    • Building a Data Lake with AWS Lake Formation: Gain hands-on experience in constructing a data lake using AWS Lake Formation.
    • Additional Lake Formation Configurations: Delve into advanced configurations and best practices.
    • Architecture and Course Review: Review architectural considerations and key takeaways.

  • Building Batch Data Analytics Solutions on AWS:

    • Overview of Data Analytics and the Data Pipeline: Understand the components of data analytics pipelines.
    • Introduction to Amazon EMR: Learn about Amazon EMR and its role in data analytics.
    • Data Analytics Pipeline Using Amazon EMR: Explore ingestion, storage, and processing of data using EMR.
    • High-Performance Batch Data Analytics with Apache Spark on Amazon EMR: Utilize Apache Spark for efficient data processing.
    • Processing and Analyzing Batch Data with Amazon EMR and Apache Hive: Leverage Apache Hive for data analysis.
    • Serverless Data Processing: Understand serverless options for data processing.
    • Security and Monitoring of Amazon EMR Clusters: Learn best practices for securing and monitoring EMR clusters.
    • Designing Batch Data Analytics Solutions: Develop strategies for effective batch data analytics.
    • Developing Modern Data Architectures on AWS: Explore modern data architecture designs.

  • Building Data Analytics Solutions Using Amazon Redshift:

    • Using Amazon Redshift in the Data Analytics Pipeline: Integrate Redshift into analytics workflows.
    • Introduction to Amazon Redshift: Get acquainted with Redshift's features and capabilities.
    • Ingestion and Storage: Learn methods for data ingestion and storage in Redshift.
    • Processing and Optimizing Data: Optimize data processing within Redshift.
    • Security and Monitoring of Amazon Redshift Clusters: Implement security measures and monitor cluster performance.
    • Designing Data Warehouse Analytics Solutions: Architect data warehouse solutions using Redshift.

  • Building Streaming Data Analytics Solutions on AWS:

    • Introduction to Streaming Data Analytics: Understand the principles of streaming data analytics.
    • Building Streaming Data Analytics Solutions with Amazon Kinesis: Utilize Kinesis for real-time data processing.
    • Building Streaming Data Analytics Solutions with Amazon MSK: Leverage Managed Streaming for Apache Kafka (MSK).
    • Integrating Streaming Data Solutions with AWS Services: Combine streaming solutions with other AWS services like AWS Glue and AWS Lambda.
    • Streaming Data Ingestion, Stream Storage, and Stream Processing: Delve into components of streaming data pipelines.
    • Security, Performance, and Cost Management Best Practices: Apply best practices for security, performance, and cost optimization.

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Course: SC-200: Microsoft Security Operations Analyst
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