This comprehensive program is designed to equip you with the skills and knowledge required to become a proficient data engineer, capable of designing, building, and managing robust data pipelines and processing solutions on the Amazon Web Services (AWS) platform.

Course Objectives: In this course, we aim to achieve the following objectives:

1. AWS Fundamentals: We will begin by providing you with a solid foundation in AWS services and architecture. You will gain an understanding of core AWS offerings, such as Amazon EC2, Amazon S3, Amazon RDS, Amazon Redshift, and AWS Lambda, which are crucial for data engineering solutions.
2. Data Storage and Management: Explore various data storage options available on AWS, including Amazon S3, Amazon DynamoDB, and Amazon RDS. You will learn how to design and implement scalable and cost-effective data storage solutions to meet specific business requirements.
3. Data Transformation and Processing: Dive into data transformation and processing techniques using AWS services like AWS Glue, AWS Data Pipeline, and AWS Lambda. You will understand how to clean, enrich, and transform raw data into valuable insights for analysis and reporting.
4. Big Data Technologies: Learn how to handle large-scale data processing using AWS big data technologies such as Amazon EMR (Elastic MapReduce) and Amazon Redshift. We will cover distributed data processing frameworks like Apache Spark and Hadoop to tackle complex data engineering tasks.
5. Real-time Data Streaming: Discover how to process and analyze real-time data streams using AWS Kinesis. You will explore different Kinesis services, including Kinesis Data Streams, Kinesis Data Firehose, and Kinesis Data Analytics.
6. Data Orchestration and Workflow: Master the art of orchestrating data pipelines and workflows with AWS Step Functions and AWS Glue. You will learn how to schedule, monitor, and manage data processing jobs efficiently.
7. Data Quality and Governance: Understand the importance of data quality and governance in data engineering. Learn how to implement data validation, data lineage, and data security measures to ensure data integrity and compliance.
8. Serverless Data Engineering: Explore serverless data engineering solutions using AWS Lambda, Amazon SQS, and Amazon S3. You will discover how serverless architecture can simplify data processing and reduce operational overhead.
9. Data Warehousing: Delve into the world of data warehousing with Amazon Redshift, a powerful, fully managed data warehouse service on AWS. Learn how to design and optimize data warehouse schemas for high-performance analytics.
10. Real-World Projects: Throughout the course, you will work on hands-on projects that simulate real-world data engineering scenarios. These projects will allow you to apply your knowledge and skills to solve practical data engineering challenges.