In the "Introduction to Designing Data Lakes on AWS" course, you will embark on a comprehensive journey, understanding the creation and operation of data lakes in a secure and scalable manner, even without prior data science knowledge. Delve into the "WHY" of data lakes, explore their value proposition, characteristics, and components, and learn about best practices to avoid common mistakes. Gain insight into ingesting and organizing data, optimizing data processing for enhanced performance and cost efficiency at scale, and building a secure and scalable architecture for data lake components. This course is designed for professionals, including Architects, System Administrators, and DevOps, aiming to design and construct a robust data lake architecture. Through practical use cases, contrast the significance of a data lake with traditional server and storage infrastructure.
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In this course, you will cover essential topics such as understanding data lake value, data ingestion and organization, data processing, and building a secure and scalable architecture.
Week 1: Obtain a foundational understanding of data lakes, their value, characteristics, and components. Learn the essentials of ingesting and organizing data, and gain insights into data processing for optimized performance and cost-efficient consumption at scale.
Week 2: Explore AWS data lake-related services, including Amazon S3, Glue Data Catalog, services for data movement, data processing, analytics, and predictive analytics. Delve into AWS Lake Formation and undergo a practical exercise walkthrough.
Week 3: Learn to utilize the right tools for data processing, understand data structure, and explore data streaming and batch data ingestion with various AWS services. Gain insights into the importance of data cataloging and review ingestion aspects in data lake architectures.
Week 4: Dive into data preparation and AWS Glue Jobs, file optimizations, and utilizing S3, Glue, and Athena for obtaining insights. Explore data lake security, the power of data visualization, and get introduced to Amazon QuickSight. Conclude with a capstone project and post-course surveys.
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