The fundamental-level course is typically designed for individuals with a basic understanding of data storage and processing concepts but little to no prior experience with building data lakes on AWS specifically. After a brief introduction to Data Lakes, we'll introduce data ingestion, cataloging and preparation, concluding with an overview of querying data with Amazon Athena. The course will continue with an AWS Lake Formation overview, including a hands-on lab where you'll build a data lake. We'll then introduce data processing and analytics leveraing AWS Glue before diving into automated data lake creatiokn using Lake Formation blueprints. Finally, we'll close with Modern Data Architectures on AWS with a lab that covers publishing and consuming data products as a service.
Syllabus 6
Module 1: Introduction to Data Lakes 1.5 hours
This module provides an overview of data lakes, their purpose, and how they differ from data warehouses. It also covers the components and architectures involved in data lakes.
Module 2: Data ingestion, cataloging, and preparation 1.5 hours
This module focuses on the processes of ingesting data into a data lake, cataloging the data, and preparing it for analysis. It covers topics such as data lake storage, data ingestion methods, crawling and cataloging data, data formatting, partitioning, compression, and querying data with Amazon At…
Module 3: Building a data lake with AWS Lake Formation 2 hours
This module introduces AWS Lake Formation, a service that helps build and manage data lakes on AWS. It covers the basic permission model, and provides an overview of the service’s features and capabilities.
Module 4: Data processing and analytics 1.5 hours
This module covers data transformation techniques and tools like AWS Glue for processing and analyzing data in the data lake. It includes hands-on demos and a technical talk on Glue and Athena Federated Queries.
Module 5: AWS Lake Formation additional configurations and capabilities 2 hours
This module explores advanced features and configurations of AWS Lake Formation, including blueprints, workflows, and fine-grained access control. It also covers data visualization with Amazon QuickSight.
Module 6: Modern data architecture on AWS 3 hours
This module introduces the concept of modern data architecture and its implementation on AWS. It covers data movement scenarios, data sharing models, and relevant readings.
Advantages and disadvantages
Advantages
Structured courses with graded quizzes, assignments and deadlines you can reset.
A shareable certificate from the university or company when you pay.
Financial aid is often approved for students in India (apply 15 days before you need it).
From Amazon Web Services, a well-regarded name.
Rated 4.7 out of 5 by 312 learners.
Self-paced: start any time.
A clear syllabus (6 parts) you can see before you start.
Hands-on: you build or practise, not just watch.
Disadvantages
Paid after a 7-day free trial (Coursera Plus or per course).
Some courses can be audited for free, but graded work and certificates need payment.
Some points apply to every course of this kind; see how we rank.
This course is paid. Here is how to take it for free
Free trials: You can start a 7-day free trial for many individual courses, Specializations, or a Coursera Plus subscription to test full course features. Cancel before the seventh day if you do not want to be charged.
Financial aid: If you cannot afford the fee for a certificate, you can apply for financial aid through the link on the course home page by filling out an application about your background and goals.
Taught by
Rafael LopesPrincipal Cloud Technologist, AWS Training & Certification
Alex G.Senior Cloud Technologist, Amazon Web Services
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