AWS Data Analytics Fundamentals Certification Course

Enroll for 1-day AWS Data Analytics Fundamentals certification training course from Koenig Solutions accredited by AWS. AWS Data Analytics fundamentals course is designed to learn the process for planning data analysis solutions and the various data analytics processes that are involved.

Through a blend of hands-on labs and interactive lectures, you will be able to learn five key factors that indicate the need for specific AWS services in collecting, processing, analyzing and presenting your data. This includes learning basic architectures, value propositions, and potential use cases. The course introduces you to the AWS services and solutions to help you build and enhance data analysis solutions.

Target Audience:

• Candidate working as Data Architect
• Candidate working as Data Scientist
• Candidate working as Data Analyst

Learning Objectives

After completing this course, you will be able 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