Building Data Lakes on AWS in Netherlands
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 payment options available
Flexible training available
4.9/57682 EnrolledEnrolled Learners
4.9/57682 EnrolledEnrolled Learners
Overview
What will you learn from us:
- Understand the core components that define a modern data lake
- Explore common architecture models used in AWS data lake implementations
- Use AWS Glue to efficiently gather, clean, and organize data
- Build comprehensive data catalogs with AWS Glue crawlers
- Manage and process large volumes of data using AWS Lake Formation
- Execute SQL queries on your data lake through Amazon Athena
- Configure fine-grained permissions and tag-based access control 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 Objectives
After finishing the course, you will be able to:
1
Learn the benefits of using data lakes for modern data management
2
Compare data lakes with traditional data warehouse solutions
3
Use AWS Glue crawlers to build searchable data catalogs
4
Analyze data directly in lakes using Amazon Athena
5
Create secure, scalable data lakes with Lake Formation
6
Apply fine-grained access controls using Lake Formation
7
Design and deliver data products through Lake Formation 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 course on Building Data Lakes on AWS in Netherlands offers learners hands-on skills to design, implement, and manage scalable data lake solutions. Participants gain expertise using AWS services such as Amazon S3, Glue, and Athena. You work with both structured and unstructured datasets, mastering data ingestion, storage, cataloging, and querying to enable advanced analytics and business intelligence.
Our learners discover that key data lake components include data ingestion, storage, cataloging, processing, and access control. Participants use services like Amazon S3 to store raw data, AWS Glue to catalog and transform it. We teach you tools like Athena for querying, creating a centralized system that supports scalable, real-time data analytics.
Our Building Data Lakes on AWS course in Netherlands teaches automating metadata creation using AWS Glue crawlers. We teach you how to configure crawlers to scan datasets stored in Amazon S3, detect schema details, and populate the AWS Glue Data Catalog. This streamlines data discovery and enables fast querying without manual tagging and is ideal for managing large, diverse datasets.
Amazon Athena is a serverless query tool that lets you analyze data in your AWS data lake using SQL. In our AWS data lakes training, learners use Athena to run queries directly on S3-stored data without setting up servers. It is ideal for real-time reporting, ad hoc analysis, and deriving insights from structured or semi-structured data.
Industries like finance, telecom, healthcare, logistics, and retail benefit greatly from AWS data lakes. Our graduates help organizations harness big data for predictive analytics, real-time decision-making, and operational efficiency. With scalable cloud-based architecture, companies modernize data management and support digital transformation across the Netherlands and globally.
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