by Noah Gift, Chris Brousseau, Robert Jordan | Publisher: Pearson IT Certification | Release Date: June 2019 | ISBN: 9780135772324

11.5+ Hours of Video InstructionAWS 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.Download Project Code from GitHub Download Code From GitHubDescriptionThis 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 LevelIntermediate level developersWhat You Will LearnStudents will:Learn how to perform collection tasks on AWSLearn how to use the appropriate storage solution for Big Data on AWSLearn how to perform processing tasks on the AWS platformLearn how to couple Visualization, Analysis, and Data Security to reason about Big Data on AWSLearn how to think about the AWS Big Data Certification exam to optimize for the best outcome.Who Should Take This CourseYou 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 RequirementsPrerequisites:1-2 years of experience with AWS and six months using Big Data tools: Spark, Hadoop, and PythonIdeally, candidates would have already passed the AWS Cloud Practitioner certAccess to a modern web browserAWS AccountAbout Pearson Video TrainingPearson 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 http://www.informit.com/video.

1. Lesson 1: Introduction and Goals
   • Learning objectives 00:01:46
   • 1.1 Learn the answer to “What is Big Data?” 00:05:28
   • 1.2 Explore the history of Big Data 00:02:34
   • 1.3 Know AWS Certification: 6 domain areas 00:03:50
   • 1.4 Understand the AWS Certification Exam: blueprint 00:03:32
   • 1.5 Learn an exam strategy 00:05:56
   • 1.6 Identify focus areas 00:02:26
   • 1.7 Learn exam tips & tricks 00:03:25
   • 1.8 Learn how to register for an AWS Certification Exam 00:02:02
2. Lesson 2: AWS Domain 1: Collection
   • Learning objectives 00:01:09
   • 2.1 Introduction / overview 00:02:31
   • 2.2 Concepts 00:02:49
   • 2.3 Approaches to data collection 00:06:31
   • 2.4 Scenario 1 00:02:51
   • 2.5 Scenario 2 00:07:47
   • 2.6 Scenario 3 00:14:37
   • 2.7 Demo - AWS Kinesis 00:28:15
   • 2.8 Review / conclusion 00:08:03
3. Lesson 3: AWS Domain 2: Storage
   • Learning objectives 00:00:46
   • 3.1 Optimize the operational characteristics of the storage solution 00:02:57
   • 3.2 Determine data access and retrieval patterns 00:04:08
   • 3.3 Evaluate mechanisms for capture, update, and retrieval of catalog entries 00:03:14
   • 3.4 Determine appropriate data structure and storage format 00:04:56
   • 3.5 Understand storage & database fundamentals 00:04:46
   • 3.6 Learn S3: storage 00:05:19
   • 3.7 Understand Glacier: backup & archive 00:02:10
   • 3.8 Create AWS Glue: data catalog 00:05:44
   • 3.9 Use Dynamodb 00:06:00
4. Lesson 4: AWS Domain 3: Processing
   • 4.1 Identify the appropriate data processing technology for a given scenario 00:04:23
   • 4.2 Design and architect the data processing solution 00:02:56
   • 4.3 Determine the operational characteristics of the solution implemented 00:03:37
   • 4.4 Understand AWS processing: overview 00:04:14
   • 4.5 Understand Elastic MapReduce (EMR) 00:01:39
   • 4.6 Learn about Apache Hadoop 00:01:19
   • 4.7 Apply EMR: architecture 00:02:59
   • 4.8 Understand EMR: operations 00:02:25
   • 4.9 Use EMR: Hive 00:01:42
   • 4.10 Use EMR: Hbase 00:01:53
   • 4.11 Use EMR: Presto 00:01:24
   • 4.12 Use EMR: Spark 00:01:59
   • 4.13 Implement EMR: storage & compression 00:01:33
   • 4.14 Implement EMR: Lambda 00:02:15
5. Lesson 5: AWS Domain 4: Analysis
   • Learning objectives 00:02:01
   • 5.1 Determine how to design and architect the analytical solution 00:08:10
   • 5.2 Understand Redshift overview 00:13:15
   • 5.3 Learn Redshift design 00:37:31
   • 5.4 Use Redshift data ingestion 00:30:26
   • 5.5 Apply Redshift operations 00:14:07
   • 5.6 Use AWS Elasticsearch: operational analytics 00:14:51
   • 5.7 Understand Machine Learning: clustering and regression 00:10:15
   • 5.8 Use AWS Athena: interactive analytics 00:18:59
6. Lesson 6: AWS Domain 5: Visualization
   • Learning objectives 00:01:19
   • 6.1 Overview of AWS Quicksight 00:16:42
   • 6.2 Design and create the Visualization platform 00:20:54
   • 6.3 Optimize the QuickSight operations 00:04:47
   • 6.4 Understand critical Quicksight limitations 00:11:29
7. Lesson 7: AWS Domain 6: Data Security
   • Learning objectives 00:01:08
   • 7.1 Data governance 00:03:46
   • 7.2 AWS Shared Responsibility Model 00:04:47
   • 7.3 Identity and Access Management (IAM) 00:24:37
   • 7.4 Encryption 00:10:06
   • 7.5 Configure VPC 00:17:15
   • 7.6 Implement Redshift Security 00:25:47
   • 7.7 Implement EMR Security 00:29:07
8. Lesson 8: Case Studies
   • Learning objectives 00:00:43
   • 8.1 Understand Big Data for Sagemaker 00:15:00
   • 8.2 Learn Sagemaker and EMR Integration 00:02:19
   • 8.3 Learn Serverless Production Big Data application development 00:26:42
   • 8.4 Implement Containerization for Big Data 00:03:26
   • 8.5 Implement Spot Instances for Big Data Pipeline 00:08:02
9. Lesson 9: Exam Prep
   • Learning objectives 00:00:33
   • 9.1 Prepare for the exam 00:00:50
   • 9.2 Walk through the exam sections 00:04:38
   • 9.3 Review the exam 00:00:21
10. Lesson 10: Course Summary
    • 10.1 Wrap up 00:16:13
11. Summary
    • Summary 00:01:18



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