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⇱ Processing and Analyzing Big Data in AWS | Coursera

Processing and Analyzing Big Data in AWS

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Processing and Analyzing Big Data in AWS

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

9 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

9 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Gain proficiency in AWS services like EMR, SageMaker, and Lambda for big data processing.

  • Learn to configure and run real-time analytics with Kinesis Analytics and Elasticsearch.

  • Understand how to migrate and transform data using AWS services like Data Pipeline and Glue.

  • Master data visualization techniques with tools like Kibana and Redshift.

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Recently updated!

May 2026

Assessments

4 assignments

Taught in English

Build your subject-matter expertise

This course is part of the AWS Certified Big Data - Specialty Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 2 modules in this course

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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.

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.

What's included

10 videos2 readings1 assignment

10 videos•Total 266 minutes
  • EMR Essentials•25 minutes
  • Use Hue and Hive with EMR•19 minutes
  • Spark and EMR•17 minutes
  • SageMaker Essentials•24 minutes
  • Training with SageMaker Notebooks•23 minutes
  • AWS Lambda Essentials•28 minutes
  • Processing Data with Lambda•24 minutes
  • Data Pipeline Essentials•38 minutes
  • Database Migration Essentials•34 minutes
  • Glue Essentials•32 minutes
2 readings•Total 20 minutes
  • Introduction to the Course 'Processing and Analyzing Big Data in AWS'•10 minutes
  • Full Specialization Resources•10 minutes
1 assignment•Total 15 minutes
  • Processing - Assessment•15 minutes

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.

What's included

6 videos1 reading3 assignments

6 videos•Total 165 minutes
  • Elasticsearch Essentials•28 minutes
  • Elasticsearch and Kibana•20 minutes
  • Athena Essentials•29 minutes
  • Redshift Essentials•31 minutes
  • Loading and Unloading Data in Redshift•34 minutes
  • Kinesis Analytics Essentials•23 minutes
1 reading•Total 10 minutes
  • Conclusion to the Course 'Processing and Analyzing Big Data in AWS'•10 minutes
3 assignments•Total 90 minutes
  • Analysis - Assessment•15 minutes
  • Full Course Assessment•60 minutes
  • Full Course Practice Assessment•15 minutes

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Frequently asked questions

Processing and Analyzing Big Data in AWS involves utilizing AWS services to manipulate, process, and analyze large data sets. This includes tools like Amazon EMR, SageMaker, Lambda, Athena, and Redshift. It is relevant because big data is integral to modern business insights and decisions, and knowing how to handle, process, and analyze this data using AWS is essential for leveraging cloud technologies and data-driven solutions.

This course covers the foundational AWS services used to process and analyze big data. It dives into tools like Amazon EMR for big data clusters, AWS Lambda for serverless processing, SageMaker for machine learning, and Redshift for data warehousing. The course also explores key services like Glue for ETL jobs, Athena for query-based analysis, and Kinesis for real-time analytics. It provides practical demonstrations on setting up, managing, and optimizing these services for big data workflows.

After completing this course, you will be proficient in processing and analyzing big data using AWS tools. You will be able to set up and manage EMR clusters, use SageMaker for machine learning models, process data with Lambda, and perform advanced queries with Athena and Redshift. You will also have the ability to design data pipelines and use AWS Glue for ETL processes, as well as implement real-time analytics using Kinesis Analytics.

A basic understanding of cloud computing and AWS services will be helpful for this course. Familiarity with data processing, data analysis concepts, and an introductory level knowledge of machine learning can provide a solid foundation. If you're new to AWS, it's recommended to have some familiarity with its core services like EC2, S3, and IAM to get the most out of this course.

This course is designed for professionals in data engineering, cloud architecture, and machine learning, as well as anyone looking to specialize in big data processing and analysis on AWS. It is ideal for individuals who work with large datasets and need to understand how to process and analyze this data efficiently using AWS cloud technologies.

The course is structured to be completed in approximately 9 hours. It consists of video lectures, hands-on labs, and demonstrations to help learners grasp the key concepts of big data processing and analysis using AWS.

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.

If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

This course is currently available only to learners who have paid or received financial aid, when available.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,