Data Engineering on AWS

Course Information

Course Name

AWS-DATA EN: Data Engineering on AWS

Exam code

DEA-C01

Duration

3 Days

Certification

AWS Certified Data Engineer – Associate

Overview

Data Engineering on AWS is a 3-day intermediate course, designed for professionals seeking a deep dive into data engineering practices and solutions on AWS. Through a balanced combination of theory, practical labs, and activities, participants learn to design, build, optimize, and secure data engineering solutions using AWS services. From foundational concepts to hands-on implementation of data lakes, data warehouses, and both batch and streaming data pipelines, this course equips data professionals with the skills needed to architect and manage modern data solutions at scale.

• Course level: Intermediate

• Duration: 3 days

Audience Profile

This course is designed for professionals who are interested in designing, building, optimizing, and securing data engineering solutions using AWS services.

Prerequisites

We recommend that attendees of this course have:

·       Familiarity with basic machine learning concepts, such as supervised and unsupervised learning, regression, classification, and clustering algorithms.

·       Working knowledge of Python programming language and common data science libraries like NumPy, Pandas, and Scikit-learn.

·       Basic understanding of cloud computing concepts and familiarity with the AWS platform.

·       Familiarity with SQL and relational databases is recommended but not mandatory.

·       Experience with version control systems like Git is beneficial but not required.

At Course Completion

In this course, you will learn to do the following:

• Understand the foundational roles and key concepts of data engineering, including data personas, data discovery, and relevant AWS services.

• Identify and explain the various AWS tools and services crucial for data engineering, encompassing orchestration, security, monitoring, CI/CD, IaC, networking, and cost optimization.

• Design and implement a data lake solution on AWS, including storage, data ingestion, transformation, and serving data for consumption.

• Optimize and secure a data lake solution by implementing open table formats, security measures, and troubleshooting common issues.

• Design and set up a data warehouse using Amazon Redshift Serverless, understanding its architecture, data ingestion, processing, and serving capabilities.

• Apply performance optimization techniques to data warehouses in Amazon Redshift, including monitoring, data optimization, query optimization, and orchestration.

• Manage security and access control for data warehouses in Amazon Redshift, understanding authentication, data security, auditing, and compliance.

• Design effective batch data pipelines using appropriate AWS services for processing and transforming data.

• Implement comprehensive strategies for batch data pipelines, covering data processing, transformation, integration, cataloging, and serving data for consumption.

• Optimize, orchestrate, and secure batch data pipelines, demonstrating advanced skills in data processing automation and security.

• Architect streaming data pipelines, understanding various use cases, ingestion, storage, processing, and analysis using AWS services.

• Optimize and secure streaming data solutions, including compliance considerations and access control.

Course Outline

Module 1 : Data Engineering Roles and Key Concepts

·       Role of a Data Engineer

·       Key functions of a Data Engineer

·       Data Personas

·       Data Discovery

·       AWS Data Services

Module 2 : AWS Data Engineering Tools and Services

·       Orchestration and Automation

·       Data Engineering Security

·       Monitoring

·       Continuous Integration and Continuous Delivery

·       Infrastructure as Code

·       AWS Serverless Application Model

·       Networking Considerations

·       Cost Optimization Tools

Module 3 : Designing and Implementing Data Lakes

·       Data lake introduction

·       Data lake storage

·       Ingest data into a data lake

·       Catalog data

·       Transform data

·       Server data for consumption

·       Hands-on lab: Setting up a Data Lake on AWS

 Module 4 : Optimizing and Securing a Data Lake Solution

·       Open Table Formats

·       Security using AWS Lake Formation

·       Setting permissions with Lake Formation

·       Security and governance

·       Troubleshooting

·       Hand-on lab: Automating Data Lake Creation using AWS Lake Formation Blueprints

 Module 5 : Data Werehouse Architecture and Design Principles

·       Introduction to data warehouses

·       Amazon Redshift Overview

·       Ingesting data into Redshift

·       Processing data

·       Serving data for consumption

·       Hands-on Lab: Setting up a Data Warehouse using Amazon Redshift Serverless

 Module 6 : Performance Optimization Techniques for Data  Warehouses

·       Monitoring and optimization options

·       Data optimization in Amazon Redshift

·       Query optimization in Amazon Redshift

·       Orchestration options

 Module 7 : Security and Access Control for Data Warehouses

·       Authentication and access control in Amazon Redshift

·       Data security in Amazon Redshift

·       Auditing and compliance in Amazon Redshift

·       Hands-on lab: Managing Access Control in Redshift

Module 8 : Desinging Batch Data Pipelines

·       Introduction to batch data pipelines

·       Designing a batch data pipeline

·       AWS services for batch data processing

 Module 9 : Implementing Strategies for Batch Data Pipeline

·       Elements of a batch data pipeline

·       Processing and transforming data

·       Integrating and cataloging your data

·       Serving data for consumption

·       Hands-on lab: A Day in the Life of a Data Engineer

 Module 10 : Optimizing, Orchestrating, and Securing Batch Data Pipelines

·       Optimizing the batch data pipeline

·       Orchestrating the batch data pipeline

·       Securing the batch data pipeline

·       Hands-on lab: Orchestrating Data Processing in Spark using AWS Step Functions

 Module 11 : Streaming Data Architecture Patterns

·       Introduction to streaming data pipelines

·       Ingesting data from stream sources

·       Streaming data ingestion services

·       Storing streaming data

·       Processing Streaming Data

·       Analyzing Streaming Data with AWS Services

·       Hands-on lab: Streaming Analytics with Amazon Managed Service for Apache Flink

 Module 12 : Optimizing and Securing Streaming Solutions

• Optimizing a streaming data solution

• Securing a streaming data pipeline

• Compliance considerations

• Hands-on lab: Access Control with Amazon Managed Streaming for Apache Kafka

 Training Delivery Notice

All AWS certification courses are conducted by certified trainers from Iverson.

Digital Methods acts as the official training partner and assists with program consultation, registration, coordination, scheduling, and administrative arrangements to ensure a smooth and professional learning experience.