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Master AWS Data Analytics for Business Insights.

Certified Data Analytics – Specialty | DAS-C01

2800,-€ (excl. VAT)


4 Days


By completing this course, you'll understand how to effectively use AWS tools to build scalable, secure, and efficient analytics solutions. You'll learn to define AWS data analytics services, integrate various tools, and leverage automation for data analysis. You'll also gain the ability to design and implement both batch and streaming data analytics solutions on AWS, ensuring you can tackle a wide range of data-related challenges.

Learning Objectives

It's recommended that you have a minimum of 2 years' experience with AWS and 5 years with common data analytics technologies. Experience with AWS services in designing, building, securing, and maintaining analytics solutions is also beneficial. While not formal prerequisites, it's strongly suggested that you have completed the AWS Certified Solutions Architect Associate and Professional course prior to this one.


This course is ideal for professionals with a background in AWS and data analytics, including data engineers, data architects, and IT professionals who are responsible for implementing data solutions. It's perfect for those seeking to validate their AWS data analytics skills with a specialty certification and for individuals looking to enhance their ability to design and manage advanced analytics solutions on AWS.

Who Should Attend

Embark on a journey to master AWS data analytics with NEOEDX's accelerated AWS Certified Data Analytics – Specialty course. Over 4 days, learn how to design, build, secure, and maintain comprehensive analytics solutions using AWS. Gain insights into effectively utilizing AWS data analytics throughout the data lifecycle, from collection and storage to processing and visualization. This course is a gateway to enhancing your business scalability and decision-making through the power of AWS analytics.

Course Description

Building Data Lakes on AWS:

  1. Introduction and fundamental concepts.

  2. Data ingestion, cataloging, and preparation.

  3. Data processing and analytics.

  4. Building with AWS Lake Formation.

  5. Additional configurations in Lake Formation.

Building Data Analytics Solutions Using Amazon Redshift:

  1. Integration in the data analytics pipeline.

  2. Introduction and overview.

  3. Data ingestion and storage.

  4. Data processing and optimization.

  5. Security and monitoring.

  6. Designing data warehouse analytics solutions.

Building Batch Data Analytics Solutions on AWS:

  1. Introduction to Amazon EMR.

  2. Batch data analytics pipeline.

  3. High-performance analytics with Apache Spark.

  4. Analyzing batch data with Hive.

  5. Serverless data processing.

  6. Security and monitoring in Amazon EMR.

  7. Designing batch analytics solutions.

Building Streaming Data Analytics Solutions on AWS:

  1. Introduction to streaming services.

  2. Real-time data analytics with Amazon Kinesis.

  3. Security, monitoring, and optimization in Kinesis.

  4. Introduction to Amazon MSK and its applications.

  5. Securing, monitoring, and optimizing Amazon MSK.

  6. Designing streaming data analytics solutions.

Course Outline

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