Introduction

As organizations increasingly rely on cloud platforms, AWS has become the go-to solution for building scalable, cost-effective, and robust data architectures. This course focuses on AWS services and best practices for data engineers to design and implement highly efficient data solutions in the cloud. From setting up data pipelines to optimizing storage and compute resources, you’ll gain practical knowledge on creating end-to-end data solutions that drive business intelligence and insights.

Prerequisites

• Basic understanding of cloud computing concepts
• Familiarity with database systems (SQL and NoSQL)
• Experience with data engineering concepts is a plus, but not required
• An AWS account for hands-on labs and exercises

Table of Contents

1. Introduction to AWS for Data Engineering
   1.1 Overview of AWS and Its Core Services
   1.2 Cloud Data Engineering: Benefits and Challenges
   1.3 Key AWS Services for Data Engineering
   – Amazon S3
   – Amazon Redshift
   – AWS Glue
   – Amazon RDS and DynamoDB
2. Setting Up AWS Data Engineering Infrastructure
   2.1 AWS Management Console and CLI for Data Engineers
   2.2 Understanding IAM Roles and Security Best Practices
   2.3 Creating and Managing Data Lakes with Amazon S3
   2.4 Optimizing AWS Resources for Cost and Performance
3. Building Scalable Data Pipelines with AWS Glue
   3.1 Introduction to AWS Glue and ETL Workflows
   3.2 Creating Glue Crawlers and Jobs
   3.3 Data Transformation Techniques with Glue
   3.4 Automating ETL Processes for Large Datasets
4. Data Warehousing with Amazon Redshift
   4.1 Setting Up Redshift Clusters and Databases
   4.2 Loading Data into Redshift from S3
   4.3 Optimizing Redshift Performance and Query Execution
   4.4 Best Practices for Redshift Data Modeling
5. Real-Time Data Processing with AWS Kinesis
   5.1 Overview of AWS Kinesis for Real-Time Data Streaming
   5.2 Setting Up Kinesis Data Streams and Firehose
   5.3 Integrating Kinesis with Other AWS Services
   5.4 Real-Time Data Analytics with AWS Lambda
6. Data Integration and API Management with AWS
   6.1 Using AWS Lambda for Serverless Data Integration
   6.2 Building APIs with Amazon API Gateway
   6.3 Data Integration Between On-Premises and Cloud Solutions
   6.4 Managing Data Workflows with Step Functions
7. Optimizing Data Storage and Retrieval in AWS
   7.1 Understanding Data Storage Options (S3, EBS, Glacier)
   7.2 Choosing the Right Storage for Different Data Types
   7.3 Implementing Data Compression and Archiving Strategies
   7.4 Optimizing Data Retrieval with Amazon Athena
8. Advanced Data Processing with AWS EMR
   8.1 Introduction to Amazon EMR for Big Data Processing
   8.2 Setting Up and Managing EMR Clusters(Ref: Azure Cloud Architect Essentials: Design and Deploy Cloud Infrastructure)
   8.3 Using Apache Spark and Hadoop on EMR
   8.4 Integrating EMR with S3 and Redshift for Seamless Data Flow
9. Machine Learning and AI for Data Engineers on AWS
   9.1 Introduction to AWS AI and Machine Learning Services
   9.2 Using Amazon SageMaker for Data Engineering and Model Training
   9.3 Integrating Data Pipelines with AI and ML Models
   9.4 Automating Data Predictions with SageMaker and Lambda
10. Security, Compliance, and Governance in AWS Data Solutions
    10.1 Implementing Data Security Best Practices in AWS
    10.2 Managing Compliance with AWS Services (HIPAA, GDPR, etc.)
    10.3 Data Encryption Strategies for Cloud Data
    10.4 Auditing and Monitoring Data Access and Usage
11. Cost Optimization in AWS Data Engineering
    11.1 Estimating Costs for Data Engineering Projects
    11.2 Strategies for Cost Optimization (Reserved Instances, Spot Instances)
    11.3 Using AWS Cost Explorer and Budgets for Resource Management
    11.4 Best Practices for Scaling Data Solutions Cost-Effectively
12. Best Practices and Future Trends in AWS Data Engineering
    12.1 Best Practices for Data Architecture in AWS
    12.2 Preparing for the Future of Data Engineering with AWS
    12.3 Keeping Up with Evolving AWS Services
    12.4 Certification Paths and Resources for AWS Data Engineers

Conclusion

By the end of this course, you will have a comprehensive understanding of how to leverage AWS services to build, optimize, and scale data engineering solutions. Whether it’s managing vast amounts of data in a data lake, processing real-time streaming data, or setting up machine learning models, you will be equipped with the tools and knowledge necessary to architect cutting-edge data solutions in the cloud. With hands-on experience and best practices, you will be well-prepared to tackle complex data engineering challenges in any organization.

Reference