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⇱ Advanced Data Processing and Analytics with AWS | Coursera

Advanced Data Processing and Analytics with AWS

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Advanced Data Processing and Analytics with AWS

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

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Master the use of Amazon Kinesis and MSK for real-time data processing.

  • Set up and manage big data workloads using Amazon EMR efficiently.

  • Build secure, scalable data lakes using AWS Lake Formation.

  • Optimize and query large datasets using Amazon Athena.

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Assessments

6 assignments

Taught in English

Build your subject-matter expertise

This course is part of the Data Engineering on AWS - The Complete 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 4 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 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.

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.

What's included

25 videos2 readings1 assignment

25 videos•Total 207 minutes
  • What Is Streaming Data?•8 minutes
  • Streaming Services in AWS•6 minutes
  • Amazon Kinesis Family•3 minutes
  • Amazon Kinesis Data Streams•13 minutes
  • Capacity Mode•7 minutes
  • Shard Iterators•13 minutes
  • Kinesis Data Generator•7 minutes
  • Data Stream Producers•4 minutes
  • Data Stream Consumer•3 minutes
  • Enhanced Fan-Out•6 minutes
  • Amazon Kinesis Firehose•19 minutes
  • Dynamic Partitioning•9 minutes
  • Data Stream vs. Data Firehose•5 minutes
  • Managed Service for Apache Flink•11 minutes
  • Flink Application•15 minutes
  • Flink Studio•4 minutes
  • Apache Kafka•10 minutes
  • Amazon Managed Service for Kafka•9 minutes
  • MSK Cluster•10 minutes
  • Kafka Topic•22 minutes
  • Send and Receive Messages•5 minutes
  • Amazon MSK Serverless•5 minutes
  • MSK Provisioned vs. Serverless•3 minutes
  • Amazon MSK Connect•4 minutes
  • Amazon Kinesis vs. Amazon MSK•6 minutes
2 readings•Total 20 minutes
  • Introduction to the Course 'Advanced Data Processing and Analytics with AWS'•10 minutes
  • Full Specialization Resource•10 minutes
1 assignment•Total 15 minutes
  • Processing Streaming Data on Amazon Kinesis and Amazon MSK - Assessment•15 minutes

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.

What's included

10 videos1 assignment

10 videos•Total 71 minutes
  • What Is Big Data?•4 minutes
  • MapReduce•5 minutes
  • Big Data Ecosystem•5 minutes
  • Amazon EMR•8 minutes
  • Storage for EMR•7 minutes
  • Creating EMR Cluster: Part 1•16 minutes
  • Creating EMR Cluster: Part 2•9 minutes
  • Migration•9 minutes
  • Amazon EMR Serverless•4 minutes
  • Cost Optimization•4 minutes
1 assignment•Total 15 minutes
  • Running Big Data Workloads on Amazon EMR - Assessment•15 minutes

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.

What's included

9 videos1 assignment

9 videos•Total 85 minutes
  • What Is a Data Lake?•9 minutes
  • Data Warehouse vs. Data Lake•7 minutes
  • AWS Lake Formation•9 minutes
  • How It Works?•10 minutes
  • Setting Up a Data Lake: Part 1•18 minutes
  • Setting Up a Data Lake: Part 2•7 minutes
  • Data Lake Permissions•13 minutes
  • Tag-Based Permissions•9 minutes
  • Open Table Formats•3 minutes
1 assignment•Total 15 minutes
  • Building Data Lakes on AWS - Assessment•15 minutes

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.

What's included

7 videos1 reading3 assignments

7 videos•Total 88 minutes
  • Why Use Amazon Athena?•7 minutes
  • How It Works?•14 minutes
  • Optimizing Queries in Athena: Part 1•16 minutes
  • Optimizing Queries in Athena: Part 2•13 minutes
  • Workgroups•14 minutes
  • Federated Query: Part 1•5 minutes
  • Federated Query: Part 2•20 minutes
1 reading•Total 10 minutes
  • Conclusion to the Course 'Advanced Data Processing and Analytics with AWS'•10 minutes
3 assignments•Total 90 minutes
  • Query Your Data Using Amazon Athena - Assessment•15 minutes
  • Full Course Assessment•60 minutes
  • Full Course Practice Assessment•15 minutes

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Frequently asked questions

Advanced Data Processing and Analytics with AWS is a comprehensive course designed to equip learners with the knowledge and skills needed to process large volumes of data using AWS services. The course covers essential topics such as streaming data, big data workloads, data lakes, and serverless analytics. With the increasing importance of real-time data processing, machine learning, and large-scale data management, the course provides invaluable expertise in using AWS tools and frameworks to process, analyze, and derive insights from data effectively. This is highly relevant for those looking to advance in fields like cloud computing, big data, and analytics.

This course focuses on advanced techniques for processing and analyzing data on AWS. It covers four key modules: processing streaming data using Amazon Kinesis and Amazon MSK, running big data workloads on Amazon EMR, building data lakes with AWS Lake Formation, and querying data using Amazon Athena. Each module includes hands-on examples that demonstrate how to work with AWS services like Kinesis, EMR, Lake Formation, and Athena to efficiently handle large datasets, perform real-time analytics, and optimize query performance.

After completing the course, you will be able to design and implement solutions for processing streaming data using AWS services, manage big data workloads with Amazon EMR, build and manage data lakes on AWS, and optimize data queries with Amazon Athena. You will have the skills to select and use appropriate AWS services to handle different types of data processing tasks, from real-time analytics to large-scale data management and querying, empowering you to tackle complex data challenges in a cloud environment.

A basic understanding of cloud computing, particularly AWS, is recommended for this course. Familiarity with data processing concepts, such as big data, data lakes, and streaming data, will be helpful but is not required. Additionally, some knowledge of SQL and general data analytics concepts will make it easier to grasp the material. However, the course is structured to help learners develop skills from the ground up, making it suitable for those with a foundational understanding of these concepts.

This course is ideal for data engineers, cloud architects, and IT professionals who want to deepen their expertise in data processing and analytics using AWS. It is also suitable for developers looking to enhance their skills in real-time data processing, big data technologies, and serverless analytics. If you're interested in working with AWS to handle complex data workloads and build scalable data solutions, this course will provide the necessary skills.

The course consists of approximately 9 hours of video content. Depending on your pace and the time you allocate for practical exercises, the course may take a bit longer to complete. It's designed for learners to complete at their own speed, with ample opportunity for hands-on practice throughout the modules.

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,