AWS Data Engineering Essentials

Day 1: Introduction to Data Engineering and AWS Cloud Ecosystem

Learning Objectives:

• Understand the foundational principles of data engineering.
• Explore the core AWS services relevant to data engineering tasks.
• Gain familiarity with the AWS management console and CLI.

Topics Covered:

• Overview of Data Engineering and its role in modern analytics.
• Introduction to AWS: Regions, Availability Zones, and Shared Responsibility Model.
• Essential AWS services for data engineering: S3, EC2, IAM, and RDS.

Day 2: Data Storage and Processing with AWS

Learning Objectives:

• Explore storage options in AWS and their use cases.
• Understand batch and stream processing for large-scale data pipelines.
• Set up foundational storage solutions for structured and unstructured data.

Topics Covered:

• Amazon S3: Storage classes, data durability, and lifecycle management.
• Amazon RDS and DynamoDB for relational and NoSQL database management.
• AWS Glue and Data Pipeline for ETL processes.

Day 3: Data Transformation and Analytics

Learning Objectives:

• Transform raw data into actionable insights using AWS analytics tools.
• Utilize serverless technologies for data analytics workflows.
• Integrate analytics tools into end-to-end data pipelines.

Topics Covered:

• Amazon Athena: Querying data stored in S3 using SQL.
• Amazon EMR: Processing large datasets with Hadoop, Spark, and Presto.
• AWS Glue: Cataloging and preparing data for analysis.

Day 4: Real-Time Data Pipelines and Streaming

Learning Objectives:

• Design and implement real-time streaming pipelines.
• Explore messaging and event-driven architectures with AWS services.
• Monitor and scale streaming pipelines for reliability and performance.

Topics Covered:

• Amazon Kinesis: Data Streams, Firehose, and Analytics.
• Integrating AWS Lambda for serverless compute in streaming pipelines.
• Amazon SNS and SQS for message queuing and notifications.

Day 5: Security, Monitoring, and Optimization

Learning Objectives:

• Secure and monitor data engineering pipelines on AWS.
• Implement best practices for cost optimization and resource management.
• Automate data engineering workflows using infrastructure as code.

Topics Covered:

• AWS security best practices: Encryption, Key Management Service (KMS), and VPCs.
• Monitoring and logging with AWS CloudWatch and CloudTrail.
• Infrastructure as Code (IaC) using AWS CloudFormation and Terraform.

Instructor Kit

(PPTX/PDF of Slides + Optional Instructor Notes)
Comprehensive slide deck with detailed content covering all modules, plus optional instructor notes to enhance teaching effectiveness.

Student Kit / Handout

(with Free Branding)
Professionally designed handouts for students, including all essential course information and customizable branding options for your organization.

Course Agenda / Outline

Detailed day-by-day course agenda and outline, ensuring smooth course delivery and a structured learning experience for students.

Study Guide

A concise guide summarizing key concepts and topics covered in the course, perfect for post-course review and exam preparation.

FAQ

Answers to commonly asked questions about the course content, delivery, and labs to support instructors and students.

Briefing Doc

A high-level document summarizing the course objectives, target audience, and key learning outcomes, ideal for internal use and marketing.

Sales Enablement Kit for IT Training Sales Engineers

(Additional Fee)
Exclusive toolkit designed for IT training sales teams, including pitch decks, objection handling, and ROI documentation to support course sales.

Course AI GPT

(Course Assistant GPT so students can talk to the course materials!)
A cutting-edge AI-driven assistant that allows students to interact with course content, ask questions, and receive instant feedback.

Optional Podcast

(of the entire course or for each individual module)
Engaging audio content covering the entire course or individual modules, perfect for on-the-go learning or reinforcement.

Lab Guide

(Lab Environments are additional and can be found at CourseLabs.io)
Step-by-step lab guide to support hands-on learning, with lab environments available separately at CourseLabs.io.

Lab Files

(If you choose to host your own lab environment)
All necessary files and instructions for setting up and running labs in your own environment, offering flexibility in deployment.

Amazon Web Services (AWS): Latest stable version.

Apache Spark: Latest stable version.

AWS Glue: Latest stable version.

Amazon Redshift: Latest stable version.

AWS Data Pipeline: Latest stable version.

Amazon S3 (Simple Storage Service): Latest stable version.

Course Objectives

This course is designed to provide a deep dive into the essential skills and knowledge required for modern data engineering on AWS. By the end of this course, participants will:

• Understand the principles of designing scalable and efficient data pipelines.
• Gain hands-on experience with AWS services such as Glue, Redshift, and SageMaker.
• Learn to implement best practices for data governance, security, and compliance.
• Develop the ability to prepare data for advanced analytics, AI/ML, and business insights.

Learning Objectives

Participants will leave this course with:

• Practical knowledge of creating and managing data transformations using AWS Glue DataBrew.
• The ability to architect, build, and optimize transactional data lakes.
• Expertise in leveraging AWS tools for real-time and batch data processing.
• Confidence in applying these skills to real-world, high-tech scenarios.

Who This Course is For

This course is ideal for:

• Data engineers and data analysts looking to enhance their AWS expertise.
• IT professionals transitioning to cloud-based data solutions.
• Business professionals aiming to understand the technical aspects of data engineering.
• Organizations seeking to upskill their teams in high-demand AWS tools and technologies.

Course Delivery and Flexibility

• 50% Lecture and 50% Hands-On Labs: A balanced approach ensures participants gain both theoretical knowledge and practical experience.
• Customizable Course Durations: This course can be delivered as a 5-day program or adapted into 4, 3, 2, or even 1-day formats to meet your organization's specific needs.

Cost

• $40 per student per day. Flexible pricing ensures accessibility while delivering high-value training content.

Take advantage of this customizable and hands-on training to empower your teams and deliver exceptional value to your clients.