Processing and Analyzing Big Data in AWS

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. This course will guide you through the essential AWS tools for processing and analyzing big data. You will learn how to leverage services such as EMR, SageMaker, Lambda, and Data Pipeline to build scalable data processing solutions. The course focuses on both the core technologies and best practices for real-time data analysis and machine learning model training in the AWS cloud. As you progress, you will dive deep into each service. You’ll set up and utilize EMR clusters with Spark, Hue, and Hive, explore machine learning workflows in SageMaker, and understand how Lambda and Glue can simplify processing and ETL jobs. Hands-on examples help you understand how to create a seamless data flow from collection to analysis. You will also be introduced to powerful tools like Elasticsearch, Athena, and Redshift for data analysis and reporting. The course is designed to equip you with the practical skills to use AWS data services effectively in production environments. Through real-world use cases, you will gain the confidence to tackle any big data challenges, from batch processing to streaming analytics. This course is ideal for data engineers, cloud developers, and IT professionals who want to enhance their data processing and analytics capabilities. A basic understanding of cloud services and programming is helpful but not required. By the end of the course, you will be able to set up data processing workflows with AWS services like EMR, SageMaker, Lambda, and Redshift, and gain proficiency in analyzing and visualizing data with Elasticsearch, Athena, and Kinesis Analytics.

Syllabus

Processing

In this module, we will dive into various AWS services for processing big data. We will cover setting up and managing EMR clusters with Spark and Hive, using SageMaker for training machine learning models, and utilizing Lambda for reactive processing. Additionally, we will explore Data Pipeline for data transformations and migrations, and discuss using Glue for ETL jobs and data catalogs.

Analysis

In this module, we will focus on AWS tools for analyzing big data. We will explore Elasticsearch and Kibana for real-time data visualization, dive into Athena for interactive querying, and examine Redshift for large-scale data warehousing. Additionally, we will cover Kinesis Analytics for processing real-time streaming data.