Building Data Lakes on AWS in Ghana
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
4.9/54595 EnrolledEnrolled Learners
4.9/54595 EnrolledEnrolled Learners
Overview
What our training includes
- Explore key components of data lakes
- Understand common lake-based architecture designs
- Ingest and organise data using AWS Glue
- Build data catalogs with AWS Glue crawlers
- Use AWS Lake Formation to process data
- Query lake data with Amazon Athena
- Configure advanced permissions and tag-based policies
Upcoming sessions
Curriculum
1
Describe the value of data lakes
2
Compare data lakes and data warehouses
3
Describe the components of a data lake
4
Recognize common architectures built on data lakes
1
Describe the relationship between data lake storage and data ingestion
2
Describe AWS Glue crawlers and how they are used to create a data catalog
3
Identify data formatting, partitioning and compression for efficient storage and query
1
Recognize how data processing applies to a data lake
2
Use AWS Glue to process data within a data lake
3
Describe how to use Amazon Athena to analyze data in a data lake
4
Lab 01: Building a Data Lake with AWS Lake Formation
1
Describe the features and benefits of AWS Lake Formation
2
Use AWS Lake Formation to create a data lake
3
Understand the AWS Lake Formation security model
4
Lab 2: Build a data lake using AWS Lake Formation
1
Explain the available built-in Blueprints to create and populate a new Lake Formation
2
Describe methods for applying advanced permissions to secure data access and workflow
3
Describe fine-grained row/cell access control
4
Explain the Lake Formation Tag-based access control mechanism and the different use cases for Named access control vs. Tag-based access control
5
Describe access flow that enforces fine-grained access policies to both catalog metadata and underlying data resource for analytics services connecting to Lake Formation
1
Explain capabilities of a modern data architecture: Scalable data lakes, Purpose-build analytics services, Seamless data movement, unified governance and performance and cost-effectiveness
2
Articulate the typical data movement within a modern data architecture: Inside out, Outside in, Around the perimeter and Sharing across
3
Describe focus of building and maintaining data products as a service
4
Describe a typical Data Mesh architecture using Lake Formation and the key enablers supporting this methodology
5
Lab 3: Building and publishing a data product in Lake Formation
1
Post course knowledge check
2
Architecture review
3
Course review
Learning Outcomes
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
Learners Point Certificate
Earn a Course Completion Certificate, an official Learners Point credential that confirms that you have successfully completed a course with us.
Prerequisites
Before enrolling in the Building Data Lakes on AWS, the eligibility requirements are as follows:
- Completed the AWS Technical Essentials classroom course
- One year of experience building data analytics pipelines or have completed the Data Analytics Fundamentals digital course
Overall ratings by our students
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Frequently asked questions
Our intermediate-level course on Building Data Lakes on AWS trains you to design and manage secure, scalable data lakes. It provides practical insights into key AWS tools such as S3, Glue, Athena, and Lake Formation. You'll acquire expertise in data ingestion, cataloging, and processing large datasets.
Additionally, you will learn to implement advanced access controls and governance practices. With hands-on practice and real-world architecture examples, the course prepares you to build efficient, analytics-ready data platforms.
Before enrolling in the Building Data Lakes on AWS, the eligibility 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
This course is ideal for data engineers, cloud architects, analysts, BI developers and ML engineers. It is also extremely beneficial for IT professionals in Ghana who manage or design data platforms in cloud environments, particularly those working with AWS.
After completing the Building Data Lakes on AWS course, you can pursue a wide range of in-demand roles within Ghana’s growing cloud and data ecosystem. Some of them are mentioned below:
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
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