Overview

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This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will gain in-depth knowledge of AWS services used for big data collection and storage. You will explore tools like Kinesis, IoT, DynamoDB, and S3, all essential in creating scalable big data solutions. By the end of the course, you will be well-versed in setting up systems for real-time data ingestion, storage optimization, and secure access management in the cloud. As the course progresses, you will delve into specialized topics such as collecting data with Kinesis Firehose, leveraging S3 for secure storage, and implementing DynamoDB for highly available databases. Practical lessons on IoT and stream processing will help you build a full-stack solution for big data workflows. You’ll also learn how to handle data encryption, permissions, and lifecycle management for efficient storage solutions. Throughout the journey, you will tackle hands-on challenges, enabling you to apply AWS’s services to real-world problems. From basic storage concepts to advanced real-time processing, you’ll be guided through each service with step-by-step explanations and actionable use cases. This course is perfect for those looking to build expertise in cloud-based big data solutions. It’s ideal for data engineers, cloud architects, and IT professionals who want to enhance their ability to design and manage robust data systems. A basic understanding of cloud services and programming is helpful but not required. By the end of the course, you will be able to configure and use AWS services like Kinesis, IoT, DynamoDB, and S3, to collect, store, and manage big data securely and efficiently in real-time environments.

Syllabus

Collection

In this module, we will explore the fundamental AWS services used to collect data, including Kinesis, IoT, and SQS. We will dive into real-time data collection with Kinesis streams and Firehose, discuss sensor data collection using IoT, and learn how to queue messages with SQS. Additionally, we will examine the role of the Kinesis agent in ingesting logs and storing data into S3.

Storage

In this module, we will focus on AWS storage solutions, primarily S3 and DynamoDB, for big data workflows. We will cover the essentials of S3, including data organization, encryption, and permissions. Additionally, we will dive into DynamoDB, exploring its tables, read/write capacity, and indexing strategies. The module also addresses optimizing storage costs and configuring reactive systems with DynamoDB streams.