Overview

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. This course equips learners with the skills to efficiently process and analyze large volumes of data using AWS services. You will gain expertise in streaming data with Amazon Kinesis and Amazon MSK, running big data workloads on Amazon EMR, building data lakes on AWS, and querying data using Amazon Athena. The course is designed to help you develop a deep understanding of AWS tools and best practices for managing data in cloud environments. Through the course, you will explore the fundamentals of streaming data and various AWS services that support real-time analytics, such as Kinesis and MSK. You’ll also dive into building scalable data lakes using AWS Lake Formation and learn how to run big data processing workloads using Amazon EMR, along with optimizing them for cost and performance. Each module builds on the last, allowing you to master streaming, storage, and query operations seamlessly. As you progress, you will learn how to configure and optimize systems for maximum throughput. The course features hands-on exercises and best practices for using AWS tools, ensuring that you develop practical skills for real-world applications. The structure ensures that you understand the foundational concepts before advancing to complex data management and optimization techniques. This course is ideal for data engineers, cloud architects, or anyone looking to advance their skills in AWS data processing. While prior experience with cloud services is helpful, the course is designed for those with an intermediate understanding of data management and analytics. By the end of the course, you will be able to configure AWS services for real-time data processing, set up data lakes, optimize big data workloads on Amazon EMR, and query data efficiently using Amazon Athena.

Syllabus

Processing Streaming Data on Amazon Kinesis and Amazon MSK

In this module, we will explore the fundamentals of real-time data streaming and dive deep into AWS services like Amazon Kinesis and Amazon Managed Streaming for Apache Kafka (MSK). You'll learn how to ingest, process, and deliver streaming data using tools such as Kinesis Data Streams, Firehose, and Flink, as well as build scalable Kafka pipelines. By the end, you'll be equipped to choose the right streaming architecture for your analytics and operational needs.

Running Big Data Workloads on Amazon EMR

In this module, we will delve into how Amazon EMR simplifies running big data frameworks like Hadoop, Spark, and Hive on AWS. You’ll learn how to configure EMR clusters, manage storage, and leverage EMR Serverless for auto-scaling workloads. The lessons also cover migration strategies and cost optimization techniques for efficient big data processing.

Building Data Lakes on AWS

In this module, we will guide you through building and managing a modern data lake on AWS using Lake Formation. You'll set up ingestion, define permissions, and manage metadata for secure, scalable data storage. We also explore the use of open table formats for analytics flexibility and performance.

Query Your Data Using Amazon Athena

In this module, we will explore how Amazon Athena enables serverless, SQL-based querying of your data stored in Amazon S3. You’ll learn to optimize queries, manage access with workgroups, and extend Athena’s capabilities through federated queries. By mastering these techniques, you'll streamline data analysis without managing infrastructure.