AWS Academy Data Analytics


AWS Academy Data Analytics comprise lab exercises to support existing big data lectures and courses that an institution is teaching or plans to teach. It is designed to help students prepare for entry-level roles in data analysis and visualization. The course provides institutions with lab exercises that will teach students how to analyze large data sets, create visual representations of that data, and publish those representations to dashboards. The course uses a case study approach to provide students with the opportunity to experience creating a real-world application of big data analysis.


Course Objectives

AWS Academy Data Analytics Labs teaches students how to:

    • Describe big data analytical concepts
    • Ingest, store, and secure data
    • Query a data store with manual schema specification
    • Query a data store with automated schema generation
    • Load and query data in a data warehouse
    • Visualize structured and unstructured data
    • Automate loading data into a data warehouse
    • Analyze unstructured data
    • Analyze IoT data

Course Outline

    • Module 01: Ingesting Data Into Amazon S3
    • Module 02: Querying Amazon S3 Data Using Amazon Athena
    • Module 03: Transforming Data Using Amazon S3, AWS Glue, and Amazon Athena
    • Module 04: Loading the Amazon Redshift Cluster With Data and Querying
    • Module 05: Delivering Insights using Amazon QuickSight
    • Module 06: Setting up and Executing a Data Pipeline Job to Load Data into Amazon S3
    • Module 07: Streaming Data with AWS Kinesis Firehose, Amazon Elasticsearch Service, and Kibana
    • Module 08: Using AWS IoT Analytics for Data Ingestion and Analysis




16 Weeks




AWS Academy Data Analytics requires a strong foundation in IT concepts and skills as can be found in the AWS Academy Cloud Foundations course.
Students should be able to:

  • Describe the difference between an online transaction processing (OLTP) system and an online
    analytical processing (OLAP) system.
  • Describe the differences between a database and a data warehouse.
  • Design a set of data objects and table relations for a simple data set.
  • Write simple data retrieval and manipulation queries with SQL.
  • Describe the five v’s of big data (Velocity, Volume, Value, Variety, and Veracity).
  • List common use cases and domains for big data solutions.
  • Normalize database design.

Students are not expected to have programming experience.


AWS Certified Data Analytics – Specialty




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Lanham, MD 20706

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