Building Data Lakes on AWS in Rwanda
Intermediate-level training from AWS
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
Easy installment options
Flexible training available
4.9/56767 EnrolledEnrolled Learners
4.9/56767 EnrolledEnrolled Learners
Overview
What you will master with us:
- Learn the foundational elements that make up a modern AWS-based data lake
- Discover typical architectural patterns used to design AWS data lake solutions
- Utilize AWS Glue to collect, prepare, and structure diverse data sources
- Create detailed metadata catalogs using automated AWS Glue crawlers
- Handle and transform massive datasets efficiently with AWS Lake Formation
- Perform interactive data analysis using Amazon Athena’s SQL-based queries
- Implement granular access controls and tag-based permission management
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 value and advantages of building data lakes for modern data management
2
Identify key differences between data lakes and conventional data warehouse systems
3
Utilize AWS Glue crawlers to generate organized and searchable data catalogs
4
Conduct data exploration and analysis within data lakes using Amazon Athena
5
Build secure, scalable data lake solutions using AWS Lake Formation services
6
Enforce precise access policies with Lake Formation’s advanced security features
7
Create and share data products efficiently through Lake Formation’s distribution 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 in Dubai, 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
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Frequently asked questions
Our Building Data Lakes on AWS in Rwanda program provides learners with practical skills to design, build, and manage scalable data lakes. Individuals master using AWS tools like Amazon S3, Glue, and Athena. Our students work with real datasets, both structured and unstructured, learning data ingestion, storage, cataloging, and querying. By the end, you’re ready to support advanced analytics and business intelligence solutions
Learning to build data lakes on AWS offers you essential skills to manage vast volumes of structured and unstructured data. Businesses are shifting to cloud-based, data-driven strategies. Our training helps you create secure, scalable solutions that support real-time analytics and decision-making. This certification course gives you an edge in today’s competitive job market.
AWS Glue simplifies building data lakes by automating data ingestion, cleaning, formatting, and cataloging. Our Building Data Lakes on AWS in Rwanda course teaches how to use Glue crawlers to create metadata automatically and partition data for faster queries. This helps you manage complex datasets efficiently and enables seamless integration with services like Amazon Athena.
You gain practical experience using AWS Glue, Lake Formation, and Athena. We guide you through ingesting data, cataloging with crawlers, setting up access controls, and designing secure, full-scale data lake environments. Our trainers let you work with real datasets and build data pipelines that reflect real business needs and best practices.
Completing our program prepares you for high-demand roles like Data Engineer, AWS Cloud Architect, or Big Data Analyst. You gain AWS-certified expertise in building modern data solutions, making you valuable in sectors like finance, telecom, and healthcare. Our training supports your growth in Rwanda’s digital economy and global job markets.
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