Data Analytics Fundamentals on AWS | August 2

Topics Covered:

In this course, you will learn how to:

• Identify the characteristics of data analysis solutions and the characteristics that indicate such a solution may be required
  
• Define types of data including structured, semistructured, and unstructured data
  
• Define data storage types such as data lakes, AWS Lake Formation, data warehouses, and the Amazon Simple Storage Service (Amazon S3)
  
• Analyze the characteristics of and differences in batch and stream processing
  
• Define how Amazon Kinesis is used to process streaming data
  
• Analyze the characteristics of different storage systems for source data
  
• Analyze the characteristics of online transaction processing (OLTP) and online analytical processing (OLAP) systems and their impact on the organization of data within these systems
  
• Analyze the differences of row-based and columnar data storage methods
  
• Define how Amazon EMR, AWS Glue, and Amazon Redshift each work to process, cleanse, and transform data within a data analysis solution
  
• Analyze the concept of atomicity, consistency, isolation, and durability (ACID) compliance as well as basic availability, soft state, eventual consistency (BASE) compliance and how an extract, transform, load (ETL) process can help to ensure compliance
  
• Explore the concept of data schemas and understand how they define data and how this information is stored in metastores
  
• Analyze the concept of data versus information
  
• Recognize the ways to analyze data to produce information for reports using tools such as Amazon QuickSight and Amazon Athena
  
• Define how AWS services work together to visualize data