Building Data Lakes on AWS in Sweden

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 tests

Hassle-free payment options

4.9/5

6694 EnrolledEnrolled Learners

4.9/5

6694 EnrolledEnrolled Learners

Overview

What our training includes:

Identify essential elements that make up a data lake
Learn typical architectural patterns for data lake solutions
Collect and structure data efficiently using AWS Glue
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

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

Meet your Trainer

Our Trainers

Learners Point has a reputation for high-quality training that makes a difference in people's lives. We undertake a practical and innovative approach to working closely with businesses to improve their workforce. Our expertise is wide-ranging with ample support from our expert trainers who are globally recognized and hold a diverse set of experiences in their field of expertise. We are proud of our instructors who take ownership of our distinctive and comprehensive training methodologies, help our students imbibe those with ease, and accomplish gracefully.

We at Learners Point believe in encouraging our students to embark upon a journey of lifelong learning and self-development, with the aid of our comprehensive and distinctive courses tailored to current market trends. The manifestation of our career-oriented approach is what we assure through a pleasant professional enriched environment with cutting-edge technology, and an outstanding while highly acknowledged training staff that uses up-to-date methodologies and quality course material. With our aim to mold professionals to be future leaders, our industry expert trainers provide the best in town mentorship to our students while endowing them with the thirst for knowledge and inspiring them to strive for professional and human excellence.

Learning Outcomes

After finishing the course, you will be able to:

Grasp the significance and benefits of developing data lakes

Distinguish between data lakes and traditional data warehouses

Use AWS Glue crawlers to build structured data catalogues

Perform data analysis in data lakes using Amazon Athena

Develop secure & scalable data lakes with AWS Lake Formation

Apply detailed access controls using Lake Formation capabilities

Design and distribute data products through Lake Formation tools

Ready to get started?

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 Sweden, 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

Building Data Lakes on AWS is an intermediate-level training program in Sweden, providing professionals with the skills and practical knowledge to manage and develop scalable data lakes using Amazon Web Services (AWS). One of the skills you learn is how to build an operational data lake that makes it easier to analyse structured and unstructured data.

Our Building Data Lakes on AWS training will enhance your skills in managing large-scale data ingestion, organising data effectively, and implementing data governance best practices. This will increase your efficiency in data engineering and analytics projects.

Our Building Data Lakes on AWS course is ideal for experienced professionals in cloud technology and data analytics. It will boost your expertise and improve your skills in implementing AWS solutions into practical applications.

Professionals can gain benefits from the Building Data Lakes on AWS certification as a beginner in the following ways:

● Understand data lake creation and management on AWS.
● Learn how to use AWS tools such as Glue, Athena, and Lake Formation.
● Become competent in entry-level data engineering, cloud architecture, and big data analytics positions.
● Develop hands-on skills in designing scalable and secure data lakes with AWS services.
● Gain certification employers recognise as proof of your competency in managing data lakes.

The process of building a data lake on AWS involves establishing a scalable, centralised storage system to accommodate both structured and unstructured data. It is crucial for managing large datasets, enabling organisations to extract valuable insights from diverse data sources.