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We're here to help you find itBuilding Data Lakes on AWS Course Overview
The "Building Data lakes on AWS" course provides a comprehensive guide to creating, managing, and utilizing Data lakes on the AWS cloud platform. It is designed to help learners understand the value of Data lakes, differentiate them from data warehouses, and recognize the crucial components that make up a data lake. The course covers essential topics such as Data ingestion, Cataloging, preparation, and processing using a variety of AWS services, including AWS Glue, Amazon Athena, and AWS Lake Formation.
Learners will gain practical experience through hands-on labs, setting up a simple data lake, building a data lake with AWS Lake Formation, Automating data lake creation, and Data Visualization using Amazon QuickSight. By the end of the course, participants will have a solid understanding of building Data lakes on AWS, and will be equipped with the skills to build a data lake on AWS effectively, ensuring they can leverage the full potential of their data assets in the cloud.
Successfully delivered 11 sessions for over 233 professionals
Purchase This Course
USD
View Fees Breakdown
Course Fee | 900 |
Exam Fee | 150 |
Total Fees (without exam) |
900 (USD) |
USD
View Fees Breakdown
Course Fee | 675 |
Exam Fee | 150 |
Total Fees (without exam) |
675 (USD) |
USD
View Fees Breakdown
Flexi Video | 16,449 |
Official E-coursebook | |
Exam Voucher (optional) | |
Hands-On-Labs2 | 4,159 |
+ GST 18% | 4,259 |
Total Fees (without exam & Labs) |
22,359 (INR) |
Total Fees (with exam & Labs) |
28,359 (INR) |
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♱ Excluding VAT/GST
You can request classroom training in any city on any date by Requesting More Information
♱ Excluding VAT/GST
You can request classroom training in any city on any date by Requesting More Information
This AWS data lake course offers in-depth training on setting up and managing data lakes, ideal for IT professionals focused on data management and analytics.
The Building Data Lakes on AWS course is designed to equip students with the skills needed to effectively construct, manage, and utilize data lakes on the AWS platform, focusing on concepts such as storage, processing, analysis, and security.