Introduction to Designing Data Lakes on AWS

Go to Course: https://www.coursera.org/learn/introduction-to-designing-data-lakes-in-aws

Introduction

**Course Review: Introduction to Designing Data Lakes on AWS** **Overview** If you’re stepping into the world of big data, understanding how to design and operate data lakes on AWS is crucial. The **Introduction to Designing Data Lakes on AWS** course on Coursera offers a comprehensive introduction to this vital skill set, intended for beginners—with no prior knowledge of data science required. Whether you're a developer looking to broaden your expertise or a business professional aiming to understand data management better, this course serves as an excellent gateway into cloud-based data architecture. **Course Structure** The course is structured over four weeks, each focusing on different facets of data lakes and their implementation on AWS. Here's a breakdown of what to expect: - **Week 1: Understanding Data Lakes** The journey begins with a solid foundation where you'll explore the motivations behind establishing a data lake, its defining characteristics, and its comparison to databases and data warehouses. This week sets the tone for the value proposition of data lakes, making it clear why they are essential in today’s data-driven world. - **Week 2: Exploring AWS Services for Data Lakes** After grasping the fundamentals, Week 2 expands your knowledge into specific AWS services suited for data lake architectures. You'll learn about essential tools like Amazon S3 for storage, AWS Glue for data integration, Amazon Athena for querying, and many others. This session emphasizes how these services work together to create a robust data lake environment. - **Week 3: Data Cataloging and Ingestion Techniques** In the third week, the course delves into data cataloging and ingestion processes. This is where you’ll learn about various services such as AWS Transfer Family, Kinesis Data Streams, and AWS Glue Crawlers. The course teaches you how to determine the appropriate timing for data processing—whether before, during, or after ingestion—validating your skills through practical scenarios. - **Week 4: Optimization and Security** Finally, Week 4 focuses on data optimization and security, crucial for maintaining performance and cost-effectiveness. You'll engage with demonstrations that illustrate best practices in optimizing your datasets. Additionally, this week covers security measures to protect your data and introduces visualization tools and datasets available on AWS for hands-on experimentation. **Recommendation** I highly recommend the **Introduction to Designing Data Lakes on AWS** course for anyone interested in data management and cloud technologies. It bridges the gap between theory and practical application effectively. Here’s why: 1. **Structured Learning Path**: The course is thoughtfully organized, ensuring a logical flow from understanding basic concepts to applying intricate AWS services. 2. **Hands-On Demos**: Learning is reinforced through practical demonstrations, which are critical for grasping abstract concepts and making them applicable in real-world scenarios. 3. **Accessibility**: With no prior experience required, the course accommodates individuals from various backgrounds, making it an inclusive resource for growth in the tech space. 4. **Industry Relevance**: As organizations increasingly rely on data lakes for analytics and big data processing, the skills acquired here will be directly applicable in many career paths. In conclusion, the **Introduction to Designing Data Lakes on AWS** course on Coursera is a valuable resource for anyone looking to demystify data lakes and leverage AWS services efficiently. It equips learners with both theoretical knowledge and practical skills, setting you on a firm foundation for future exploration in data management and analytics. Don’t hesitate—dive into this course, and unlock the potential of your data-lake projects today!

Syllabus

Week 1

Welcome to the course! In Week 1, you'll discover why you may want a Data Lake, its characteristics and components, and how it compares to other data data scenarios, such as databases and data warehouses.

In Week 2, you'll build on your knowledge of what data lakes are and why they may be a solution for your needs. You'll explore AWS services that can be used in data lake architectures, like Amazon S3, AWS Glue, Amazon Athena, Amazon Elasticsearch Service, LakeFormation, Amazon Rekognition, API Gateway and other services used for data movement, processing and visualization.

In Week 3, you'll explore specifics of data cataloging and ingestion, and learn about services like AWS Transfer Family, Amazon Kinesis Data Streams, Kinesis Firehose, Kinesis Analytics, AWS Snow Family, AWS Glue Crawlers, and others. You'll also discover when is the right time to process data--before, after, or while data is being ingested. Given scenarios, you'll be able to easily identify when to process data and match the most appropriate AWS services to each scenario.

In Week 4, you are going to dive deeper into data optimization and data processing. Demos around best practices will show you how to optimize your dataset for performance and cost--just by using the right tool for the job! You will also discover data security, data visualization tools, and AWS datasets you can use to experiment and get started.