Intermediate-level training from AWS
7 specialised modules with real-time projects
Use AWS Lake Formation to build a data lake
Learn with course lectures & hands-on labs
Gain access to high-quality study materials
Expert guided preparation with mock exams
Hassle-free payment options
Flexible training available
What our training includes:
Upcoming sessions
Describe the value of data lakes
Compare data lakes and data warehouses
Describe the components of a data lake
Recognize common architectures built on data lakes
Describe the relationship between data lake storage and data ingestion
Describe AWS Glue crawlers and how they are used to create a data catalog
Identify data formatting, partitioning and compression for efficient storage and query
Recognize how data processing applies to a data lake
Use AWS Glue to process data within a data lake
Describe how to use Amazon Athena to analyze data in a data lake
Lab 01: Building a Data Lake with AWS Lake Formation
Describe the features and benefits of AWS Lake Formation
Use AWS Lake Formation to create a data lake
Understand the AWS Lake Formation security model
Lab 2: Build a data lake using AWS Lake Formation
Explain the available built-in Blueprints to create and populate a new Lake Formation
Describe methods for applying advanced permissions to secure data access and workflow
Describe fine-grained row/cell access control
Explain the Lake Formation Tag-based access control mechanism and the different use cases for Named access control vs. Tag-based access control
Describe access flow that enforces fine-grained access policies to both catalog metadata and underlying data resource for analytics services connecting to Lake Formation
Explain capabilities of a modern data architecture: Scalable data lakes, Purpose-build analytics services, Seamless data movement, unified governance and performance and cost-effectiveness
Articulate the typical data movement within a modern data architecture: Inside out, Outside in, Around the perimeter and Sharing across
Describe focus of building and maintaining data products as a service
Describe a typical Data Mesh architecture using Lake Formation and the key enablers supporting this methodology
Lab 3: Building and publishing a data product in Lake Formation
Post course knowledge check
Architecture review
Course review
After finishing the course, you will be able to:
1
Understand the core value and purpose of building data lakes
2
Compare key differences between data lakes and data warehouses
3
Use AWS Glue crawlers to create an organised data catalog
4
Analyse data within the lake using Amazon Athena and queries
5
Create and secure scalable data lakes using AWS Lake Formation
6
Implement fine-grained access control using AWS Lake Formation features
7
Build and share data products using Lake Formation and governance tools
Before enrolling in the Building Data Lakes on AWS, the eligibility requirements are as follows:
Overall ratings by our students
Building Data Lakes on AWS Course is a specialized intermediate-level course that teaches you how to build and manage scalable data lakes using Amazon Web Services. Our course helps you utilise the full potential of AWS services to store, organize and analyse massive datasets. A data lake is a centralized repository that allows you to store structured and unstructured data.
To enrol in the AWS Data Lake course, you need to complete these eligibility requirements. These requirements are as follows:
1. Completed the AWS Technical Essentials classroom course
2. One year of experience building data analytics pipelines or have completed the Data Analytics Fundamentals digital course
Our Data Lake Course is ideal for professionals who are looking to deepen their expertise in cloud-based data architecture on AWS. Suitable candidates are as follows:
1. Data Engineers
2. Cloud Architects
3. Data Analysts and Scientists
4. Database Administrators (DBAs)
5. IT Professionals
You will learn about several core subject areas related to building a data lake in our training in AWS. These topics will enable professionals to build, secure and analyse data in the AWS cloud. These topic are:
1. Introduction to data lakes
2. Data ingestion
3. Data cataloging and metadata management
4. Data preparation
5. Security and access control
6. Querying and analytics
7. Data lake optimization and performance
After completing the Building Data Lakes on AWS training, you can avail several high-demand career roles in cloud data management, analytics and engineering. These job roles are:
1. Data Engineer
2. Cloud Data Architect
3. Big Data Engineer
4. Data Analyst
5. Machine Learning Engineer
6. ETL Developer
7. Data Platform Engineer
8. Solutions Architect
As a fresher, you can enrol in the Building Data Lakes on AWS training, if you have a strong interest in data engineering, cloud technologies or analytics. However, you must follow the eligibility criteria mentioned before enrolling in this course, as it is an intermediate-level training.
Our course begins with an introduction to data lakes, architecture and core AWS services, which makes it beginner-friendly for the candidates. You get the chance to work with real AWS tools like S3, Glue, Lake Formation, Athena. Our course prepares you with valuable cloud data handling capabilities.
The most suitable AWS service for building data lakes is Amazon S3 (Simple Storage Service). However, we build a data lake using a combination of several AWS services such as using Lake Formation, Glue and Athena to manage, prepare and analyse the data.
Yes, our Building Data Lakes on AWS training is available across different GCC countries. This helps the candidates earn this certification. The several GCC regions include:
Learn now, pay later
Dive into your course now and pay in installments

