AWS Certified Big Data - Specialty Complete Video Course and Practice Test Video Training

Video description

11.5+ Hours of Video Instruction

AWS leads the world in cloud computing and big data. This course offers the complete package to help practitioners master the core skills and competencies needed to build successful, high-value big data applications, with a clear path toward passing the certification exam AWS Certified Big Data - Specialty.
This course provides a solid foundation in all areas required to pass the AWS Certified Big Data Specialty Exam—including Collection, Storage, Processing, Analysis, Visualization, and Data Security. In addition, multiple quizzes and a practice exam prepare the student for the formal Certification Exam administered by AWS.


This course provides case study–based training, designed completely around Jupyter notebook–based learning using only AWS big data technologies. Every exercise shown in this video can be run interactively by the students watching. The material exclusively focuses on AWS, with the goal of building enough foundation that the learner can achieve certification.

Most companies struggle with widely varied, high-volume, and fast-moving data. After years of hype around big data, tools and infrastructure have improved to the point where companies are way beyond pilot projects and proofs-of-concept. Big data, where parallel processing is needed just to do the work, is the new normal. AWS leads the world in cloud computing and big data. This course offers a clear path toward certification by AWS on Big Data solutions, which is needed in a competitive job market. This course fills a known gap in this rapidly growing space.

Skill Level

  • Intermediate level developers

What You Will Learn

Students will:

  • Learn how to perform collection tasks on AWS
  • Learn how to use the appropriate storage solution for Big Data on AWS
  • Learn how to perform processing tasks on the AWS platform
  • Learn how to couple Visualization, Analysis, and Data Security to reason about Big Data on AWS
  • Learn how to think about the AWS Big Data Certification exam to optimize for the best outcome.

Who Should Take This Course

  • You are a DevOps Engineer who wants to understand how to operationalize Big Data workloads.
  • You are a Software Engineer who wants to master Big Data terminology and practices on AWS.
  • You are a Machine Learning Engineer who wants to solidify their knowledge of AWS Big Data practices.
  • You are a Product Manager who needs to understand the AWS Big Data lifecycle.
  • You are a Data Scientist who runs Big Data workloads on AWS.

Course Requirements


  • 1-2 years of experience with AWS and six months using Big Data tools: Spark, Hadoop, and Python
  • Ideally, candidates would have already passed the AWS Cloud Practitioner cert
  • Access to a modern web browser
  • AWS Account

About Pearson Video Training

Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Prentice Hall, Sams, and Que. Topics include: IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more. Learn more about Pearson Video training at

Video Lessons are available for download for offline viewing within the streaming format. Look for the green arrow in each lesson.

Table of contents

  1. Lesson 1: Introduction and Goals
    1. Learning objectives
    2. 1.1 Learn the answer to “What is Big Data?”
    3. 1.2 Explore the history of Big Data
    4. 1.3 Know AWS Certification: 6 domain areas
    5. 1.4 Understand the AWS Certification Exam: blueprint
    6. 1.5 Learn an exam strategy
    7. 1.6 Identify focus areas
    8. 1.7 Learn exam tips tricks
    9. 1.8 Learn how to register for an AWS Certification Exam
  2. Lesson 2: AWS Domain 1: Collection
    1. Learning objectives
    2. 2.1 Introduction / overview
    3. 2.2 Concepts
    4. 2.3 Approaches to data collection
    5. 2.4 Scenario 1
    6. 2.5 Scenario 2
    7. 2.6 Scenario 3
    8. 2.7 Demo - AWS Kinesis
    9. 2.8 Review / conclusion
  3. Lesson 3: AWS Domain 2: Storage
    1. Learning objectives
    2. 3.1 Optimize the operational characteristics of the storage solution
    3. 3.2 Determine data access and retrieval patterns
    4. 3.3 Evaluate mechanisms for capture, update, and retrieval of catalog entries
    5. 3.4 Determine appropriate data structure and storage format
    6. 3.5 Understand storage database fundamentals
    7. 3.6 Learn S3: storage
    8. 3.7 Understand Glacier: backup archive
    9. 3.8 Create AWS Glue: data catalog
    10. 3.9 Use Dynamodb
  4. Lesson 4: AWS Domain 3: Processing
    1. 4.1 Identify the appropriate data processing technology for a given scenario
    2. 4.2 Design and architect the data processing solution
    3. 4.3 Determine the operational characteristics of the solution implemented
    4. 4.4 Understand AWS processing: overview
    5. 4.5 Understand Elastic MapReduce (EMR)
    6. 4.6 Learn about Apache Hadoop
    7. 4.7 Apply EMR: architecture
    8. 4.8 Understand EMR: operations
    9. 4.9 Use EMR: Hive
    10. 4.10 Use EMR: Hbase
    11. 4.11 Use EMR: Presto
    12. 4.12 Use EMR: Spark
    13. 4.13 Implement EMR: storage compression
    14. 4.14 Implement EMR: Lambda
  5. Lesson 5: AWS Domain 4: Analysis
    1. Learning objectives
    2. 5.1 Determine how to design and architect the analytical solution
    3. 5.2 Understand Redshift overview
    4. 5.3 Learn Redshift design
    5. 5.4 Use Redshift data ingestion
    6. 5.5 Apply Redshift operations
    7. 5.6 Use AWS Elasticsearch: operational analytics
    8. 5.7 Understand Machine Learning: clustering and regression
    9. 5.8 Use AWS Athena: interactive analytics
  6. Lesson 6: AWS Domain 5: Visualization
    1. Learning objectives
    2. 6.1 Overview of AWS Quicksight
    3. 6.2 Design and create the Visualization platform
    4. 6.3 Optimize the QuickSight operations
    5. 6.4 Understand critical Quicksight limitations
  7. Lesson 7: AWS Domain 6: Data Security
    1. Learning objectives
    2. 7.1 Data governance
    3. 7.2 AWS Shared Responsibility Model
    4. 7.3 Identity and Access Management (IAM)
    5. 7.4 Encryption
    6. 7.5 Configure VPC
    7. 7.6 Implement Redshift Security
    8. 7.7 Implement EMR Security
  8. Lesson 8: Case Studies
    1. Learning objectives
    2. 8.1 Understand Big Data for Sagemaker
    3. 8.2 Learn Sagemaker and EMR Integration
    4. 8.3 Learn Serverless Production Big Data application development
    5. 8.4 Implement Containerization for Big Data
    6. 8.5 Implement Spot Instances for Big Data Pipeline
  9. Lesson 9: Exam Prep
    1. Learning objectives
    2. 9.1 Prepare for the exam
    3. 9.2 Walk through the exam sections
    4. 9.3 Review the exam
  10. Lesson 10: Course Summary
    1. 10.1 Wrap up
  11. Summary
    1. Summary

Product information

  • Title: AWS Certified Big Data - Specialty Complete Video Course and Practice Test Video Training
  • Author(s): Robert Jordan, Chris Brousseau, Noah Gift
  • Release date: June 2019
  • Publisher(s): Pearson IT Certification
  • ISBN: 0135772354