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⇱ AWS Data, Integration and Modern Workloads | Coursera

AWS Data, Integration and Modern Workloads

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AWS Data, Integration and Modern Workloads

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

Recommended experience

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

Recommended experience

5 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Master AWS database services (RDS, DynamoDB, Aurora, Redshift) and their use cases.

  • Integrate applications and workflows using SNS, MQ, SQS, and Step Functions.

  • Analyze data and develop machine learning models using AWS services like Athena, SageMaker, and Rekognition.

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Recently updated!

April 2026

Assessments

7 assignments

Taught in English

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This course is part of the AWS Cloud Practitioner CLF-C02 Exam Training Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 5 modules in this course

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'll dive into AWS's powerful data services and tools for modern workloads. Starting with Amazon RDS, you'll explore how to set up and manage relational databases on AWS. You'll also gain an understanding of advanced AWS database solutions, such as Aurora, DynamoDB, Redshift, and ElastiCache. Each of these services is tailored to different use cases, allowing you to choose the right database solution for your applications. Next, the course covers AWS Route 53 for DNS and domain management, enabling you to ensure high availability and failover capabilities for your websites and services. You will also explore Application Integration Services, including SNS, MQ, SQS, Step Functions, and SWF—all designed to integrate and coordinate your applications and workflows seamlessly. The course also dives into AWS's powerful Analytics tools like Athena, Kinesis, Elasticsearch, Glue, and QuickSight, which help you analyze large datasets, stream real-time data, and create insightful visualizations. You’ll also explore how Machine Learning services such as Rekognition, Polly, Translate, Transcribe, Comprehend, and SageMaker empower developers to build intelligent, scalable applications with advanced capabilities like image recognition, translation, text-to-speech, and NLP. This course is perfect for developers, data engineers, and professionals looking to expand their knowledge of AWS and modern data and machine learning workloads. While no formal prerequisites are required, familiarity with basic cloud computing concepts is beneficial. The course is designed for an intermediate skill level, ideal for those who want to level up their knowledge of AWS data services and machine learning tools. By the end of the course, you will be able to deploy, manage, and integrate databases on AWS, set up and use analytics and machine learning services, and design modern, scalable cloud workloads using AWS tools.

In this module, we will introduce AWS database services, focusing on Amazon RDS and its use for managing relational databases. You’ll also gain hands-on experience with database creation and learn about other advanced database solutions like Aurora, DynamoDB, and Redshift. By the end, you’ll understand how to choose the right database service based on application needs and scalability requirements.

What's included

5 videos2 readings1 assignment

5 videos•Total 56 minutes
  • Introduction to Amazon RDS•21 minutes
  • Create an RDS Database – Lab•14 minutes
  • Amazon Aurora, DynamoDB, Redshift, and ElastiCache•13 minutes
  • Database Migration Service (DMS)•3 minutes
  • Additional Database Services•6 minutes
2 readings•Total 20 minutes
  • Introduction to the Course 'AWS Data, Integration and Modern Workloads'•10 minutes
  • Full Specialization Resource•10 minutes
1 assignment•Total 15 minutes
  • AWS Database Services - Assessment•15 minutes

In this module, we will explore Amazon Route 53, AWS’s scalable and highly available DNS web service. You’ll learn how to configure Route 53 for domain registration, routing policies, and ensuring failover mechanisms. By the end, you'll be equipped to manage the DNS needs of your web applications effectively.

What's included

1 video1 assignment

1 video•Total 20 minutes
  • Amazon Route 53 and DNS•20 minutes
1 assignment•Total 15 minutes
  • Amazon Route 53 - Assessment•15 minutes

In this module, we will dive into AWS’s application integration services, starting with Amazon SNS for notifications, and moving to Amazon MQ and SQS for managing message-based workflows. Additionally, we’ll explore AWS Step Functions and SWF to streamline and orchestrate complex application workflows. You’ll gain the tools needed to integrate your applications efficiently on AWS.

What's included

3 videos1 assignment

3 videos•Total 23 minutes
  • Amazon Simple Notification Service (SNS)•12 minutes
  • Amazon MQ and Amazon SQS•7 minutes
  • AWS Step Functions and Amazon SWF•4 minutes
1 assignment•Total 15 minutes
  • Application Integration Services - Assessment•15 minutes

In this module, we will explore AWS analytics services, from querying data in Amazon S3 using Amazon Athena to processing real-time data streams with Amazon Kinesis. You’ll also gain insights into Amazon Glue for ETL processes and Amazon QuickSight for data visualizations. By the end, you’ll be prepared to leverage these tools for advanced data analytics in the cloud.

What's included

4 videos1 assignment

4 videos•Total 17 minutes
  • Amazon Athena•6 minutes
  • Amazon Kinesis•7 minutes
  • Amazon Elasticsearch•2 minutes
  • Amazon Glue and QuickSight•2 minutes
1 assignment•Total 15 minutes
  • Analytics - Assessment•15 minutes

In this module, we will introduce you to machine learning services on AWS, starting with Amazon Rekognition for analyzing images and videos. You’ll explore Amazon Polly for building speech-enabled applications and learn about Amazon SageMaker for end-to-end machine learning model development. This module will provide you with the foundational knowledge to integrate machine learning into your AWS applications.

What's included

6 videos1 reading3 assignments

6 videos•Total 21 minutes
  • Amazon Rekognition•5 minutes
  • Amazon Polly•3 minutes
  • Amazon Translate•2 minutes
  • Amazon Transcribe•5 minutes
  • Amazon Comprehend•3 minutes
  • Amazon SageMaker•2 minutes
1 reading•Total 10 minutes
  • Conclusion to the Course 'AWS Data, Integration and Modern Workloads'•10 minutes
3 assignments•Total 90 minutes
  • Machine Learning on AWS - Assessment•15 minutes
  • Full Course Assessment•60 minutes
  • Full Course Practice Assessment•15 minutes

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Frequently asked questions

AWS Data, Integration & Modern Workloads is a specialization that covers the essential AWS services related to databases, application integration, analytics, and machine learning. It is relevant because modern workloads are increasingly reliant on scalable cloud infrastructure, and AWS provides powerful services for managing data, integrating applications, processing analytics, and implementing machine learning models. Understanding these services is crucial for handling the growing demand for data processing and AI capabilities in the cloud.

This specialization focuses on AWS services for database management, application integration, analytics, and machine learning. It includes hands-on lessons on using AWS tools like Amazon RDS, DynamoDB, and Aurora for databases, Route 53 for DNS, SNS and SQS for messaging, and Athena and Kinesis for real-time data processing. Additionally, you'll explore machine learning services such as Rekognition, Polly, and SageMaker, which empower developers to build intelligent, data-driven applications.

After completing this specialization, you will have the skills to design and implement database solutions using AWS, integrate various AWS services for application workflows, process and analyze large amounts of data, and apply machine learning techniques to enhance applications. You'll be equipped to use AWS’s tools for developing modern, scalable, and intelligent workloads, making you proficient in a wide range of AWS services for real-world applications.

A basic understanding of cloud computing concepts and experience with fundamental IT principles such as databases and networking will be beneficial. This specialization is beginner-friendly and designed for those who have some familiarity with AWS or cloud technologies. If you're new to AWS, the foundational knowledge covered in introductory AWS training will be helpful.

This specialization is designed for developers, data engineers, cloud architects, and IT professionals who want to deepen their understanding of AWS services related to data management, application integration, analytics, and machine learning. It’s ideal for those looking to build modern cloud-based workloads that leverage AWS’s powerful infrastructure and services.

The AWS Data, Integration & Modern Workloads specialization can be completed in approximately 7 hours. This includes both theoretical content and hands-on labs to ensure you gain practical experience with the various AWS services covered throughout the specialization.

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,