AWS Certified Data Engineering Associate

The AWS Certified Data Engineering - Associate (DEA -C01) course is designed to provide practical skills in the design, implementation and optimization of data pipes on AWS.

Under the practical Learning Method approach, participants will receive a cloud entrance pack to apply the concepts through workshops, laboratories and projects in real environments, and will use tools such as AWS GUE, Amazon Redshift, Kinesis, Dynamodb, SageMaker and AWS EMR. They will learn to design storage, processing and data analysis solutions for Big Data environments, Business Intelligence and Machine Learning.

This course is aimed at data engineers, data analysts, data scientists and ETL professionals who wish to be certified as AWS Certified Data Engineer Associate and develop key competitions in the cloud.

At the end of the course, participants will be able to:

• Understand the infrastructure and AWS services for data, such as Amazon S3, RDS, Dynamodb, Redshift and Gue
• Design and implement data pipes, optimizing intake, processing and storage
• Apply principles of data safety and governance, ensuring compliance with regulations
• Scale data solutions on AWS, implementing high -performance and low -cost architectures
• Automatize ETL processes in AWS, using AWS GUE, Step Functions and Lambda
• Optimize the performance in Big Data environments, with AWS EMR and Apache Spark
• Implement data analysis solutions, integrating Amazon Athena, Quicksight and Kinesis
• Prepare for AWS Certified Data Engineering - Associate certification, validating the mastery of the best data engineering practices in AWS

Courses

1. Certificación internacional AWS Certified Cloud Practicioner

To participate in this training, attendees must meet the following requirements:

• Having taken the AWS Academy Cloud Foundations course in matrix classroom or demonstrating equivalent experience in AWS.
• Basic knowledge in databases and SQL language.
• Familiarity with concepts of storage, processing and data analysis.

These requirements ensure that participants can focus on applying knowledge acquired in real data environments.

Learning Methodology

The learning methodology, regardless of the modality (in-person or remote), is based on the development of workshops or labs that lead to the construction of a project, emulating real activities in a company.

The instructor (live), a professional with extensive experience in work environments related to the topics covered, acts as a workshop leader, guiding students' practice through knowledge transfer processes, applying the concepts of the proposed syllabus to the project.

The methodology seeks that the student does not memorize, but rather understands the concepts and how they are applied in a work environment.

As a result of this work, at the end of the training the student will have gained real experience, will be prepared for work and to pass an interview, a technical test, and/or achieve higher scores on international certification exams.

Conditions to guarantee successful results:

• a. An institution that requires the application of the model through organization, logistics, and strict control over the activities to be carried out by the participants in each training session.
• b. An instructor located anywhere in the world, who has the required in-depth knowledge, expertise, experience, and outstanding values, ensuring a very high-level knowledge transfer.
• c. A committed student, with the space, time, and attention required by the training process, and the willingness to focus on understanding how concepts are applied in a work environment, and not memorizing concepts just to take an exam.

Pre-enrollment

You do not need to pay to pre-enroll. By pre-enrolling, you reserve a spot in the group for this course or program. Our team will contact you to complete your enrollment.

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