Building Data Lakes on AWS in Kenya
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
4.9/54604 EnrolledEnrolled Learners
4.9/54604 EnrolledEnrolled Learners
Overview
What you will learn:
- Learn to identify key elements that make up a data lake
- Understand typical architectural patterns for data lake solutions
- Mater AWS Glue for collecting and structuring data efficiently using
- Learn to create detailed data catalogues via AWS Glue crawlers
- Process large-scale data sets with AWS Lake Formation
- Run data queries on lakes using Amazon Athena
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 Nmed 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 completing the course, you will be able to:
1
Master the core value and purpose of building data lakes & learn the key differences between data lakes and data warehouses
2
Create an organised data catalogue using AWS Glue crawlers
3
Learn data analysis within the lake using Amazon Athena and queries
4
Master AWS Lake Formation and its advanced features for securing scalable data lakes & detailed access control
5
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 in Kenya, you must meet these eligibility requirements:
- 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 course for Building Data Lakes on AWS in Kenya is an intermediate-level training program. Participants in this course master Amazon Web Services (AWS) to create and oversee scalable data lakes. We train learners to build skills for building an operational data lake, facilitating the analysis of structured and unstructured data.
For enrolling in the Building Data Lakes on AWS course in Kenya, you must meet the following eligibility criteria:
- Completed the AWS Technical Essentials classroom course
- 1 year of experience building data analytics pipelines, or have completed the Data Analytics Fundamentals digital course
In our training, professionals learn about various topics that help them to build, secure and analyse data in the AWS cloud. These topics are listed below:
- Introduction to data lakes
- Data ingestion
- Data cataloguing and metadata management
- Data preparation
- Security and access control
- Querying and analytics
- Data lake optimisation and performance
After completing the Building Data Lakes on AWS training, you can pursue in-demand job roles in cloud data management, analytics and engineering. Some of the top job roles available in Kenya are listed below:
1. Data Engineer/Analyst
2. Cloud Data Architect
3. Big Data Engineer
4. ETL Developer
5. Solutions Architect
Yes, this course is ideal for learning Data Processing and Analysis, which also includes the following:
- Understand the features and benefits of AWS Lake Formation
- Master AWS Lake Formation for creating a data lake
- Get a solid understanding of the AWS Lake Formation security model
- Learn to build a data lake using AWS Lake Formation
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