Data is everywhere. If you or your company don't know what data you have and what insights you can uncover through your data, you are at a competitive disadvantage. In this course, you'll get introduced to data analytics and the upside of data-driven decisions.
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You'll learn about the omnipresence of data in today's world and what it takes to start thinking and acting like a data analyst. Week 1 concludes by comparing and contrasting ETL (Extract, Transform, Load) and ELT(Extract, Load, Transform) and where data is transformed and how data warehouses retain data. Week 2 kicks off with an overview of data workflow and database foundations. The four vs (volume, velocity, variety and veracity) of data are explained along with walk-throughs of collecting, processing, and storing data. In the course's final week, you'll get briefed on some of the AWS services that can be leveraged for ETL. You'll extract data with Amazon API Gateway, process data with AWS Lambda, load data with Amazon RDS, and visualize data with Amazon QuickSight. There's the right tool for each unique data analysis task.
This course is part of the AWS Cloud Technology Consultant Professional Certificate.
What you'll learn
- Key data types and structures
- AWS services for the ETL process
- Hands-on skills for Amazon API Gateway and Amazon QuickSight
Syllabus
Module 1: Foundations of Data Analysis
Welcome to the first module of the course. This module introduces fundamental concepts in data analysis. You begin the module with how to assess use cases for data analysis in the cloud. Then, you explore some of the main data types and structures, and learn how metadata can help you manage datasets. Lastly, you complete the module by contrasting two data-processing approaches for analytics: extract, transform, and load (ETL) and extract, load, and transform (ELT).
Module 2: ETL Pipeline and Database Foundations
In this module, you start learning about the ETL pipeline, with an emphasis on the real-world scenario. Through each step, you learn how to gather data, ensure data quality, locate the appropriate storage or database, and evaluate insights. After you examine the ETL process, you assess SQL and NoSQL databases, and interact with a hands-on activity to practice your skills.
Module 3: AWS Services for ETL
In this module, you review AWS services for data analysis, and reinforce your learning through practical labs. These services include Amazon API Gateway, Amazon Relational Database Service (Amazon RDS), Amazon DynamoDB, and Amazon QuickSight. You review these services in the AWS Management Console, and evaluate how you can use each service in the ETL process. Then, you gain practical experience by working with some of these service in a preconfigured environment.
