Introduction

Studying for any certification exam can seem daunting. AWS Certified Data Analytics Study Guide: Specialty (DAS-C01) Exam was designed and developed with relevant topics, questions, and exercises to enable a cloud practitioner to focus their precious study time and effort on the germane set of topics targeted at the right level of abstraction so they can confidently take the AWS Certified Data Analytics - Specialty (DAS-C01) exam.

This study guide presents a set of topics around the data and analytics pipeline and discusses various topics including data collection, data transformation, data storage and processing, data analytics, data visualization, and the encompassing security elements for the pipeline. The study guide also includes reference material and additional materials and hands-on workshops that are highly recommended and will aid in your overall learning experience.

What Does This Book Cover?

This book covers topics you need to know to prepare for the AWS Certified Data Analytics - Specialty (DAS-C01) exam:

• Chapter 1: History of Analytics and Big Data    This chapter begins with a history of big data and its evolution over the years before discussing the analytics pipeline and the big data reference architecture. It also talks about some key architectural principles for an analytics pipeline and introduces the concept of data lakes before introducing AWS Lake Formation to build the data lakes.
• Chapter 2: Data Collection    Data collection is typically the first step in an analytics pipeline. This chapter discusses the various services involved in data collection, ranging from services related to streaming data ingestion like Amazon Kinesis and Amazon SQS to mini-batch and large-scale batch transfers like AWS Glue, AWS Data Pipeline, and the AWS Snow family.
• Chapter 3: Data Storage    Chapter 3 discusses various storage options available on Amazon Web Services, including Amazon S3, Amazon S3 Glacier, Amazon DynamoDB, Amazon DocumentDB, Amazon Neptune, AWS Storage Gateway, Amazon EFS, Amazon FSx for Lustre, and AWS Transfer for SFTP. I not only discuss the different options but the use cases around which each one of these are suitable and when to choose one over the other.
• Chapter 4: Data Processing and Analysis    In Chapter 4, we will cover data processing and analysis technologies on the AWS stack, including Amazon Athena, Amazon EMR, Amazon Elasticsearch Service, Amazon Redshift, and Amazon Kinesis Data Analytics, before wrapping up the chapter with a discussion around orchestration tools like AWS Step Functions, Apache Airflow, and AWS Glue workflow management. I'll also compare some of the processing technologies around the use cases and when to use which technology.
• Chapter 5: Data Visualization    Chapter 5 will discuss the visualization options like Amazon QuickSight and other visualization options available on AWS Marketplace. I'll briefly touch on the AWS ML stack as that is also a natural consumer of analytics on the AWS stack.
• Chapter 6: Data Security    A major section of the exam is security considerations for the analytics pipeline, and hence I have dedicated a complete chapter to security, discussing IAM and security for each service available on the Analytics stack.

Preparing for the Exam

AWS offers multiple levels of certification for the AWS platform. The basic level for the certification is the foundation level, which covers the AWS Certified Cloud Practitioner exam.

We then have the associate-level exams, which require at least one year of hands-on knowledge on the AWS platform. At the time of this writing, AWS offers three associate-level exams:

• AWS Certified Solutions Architect Associate
• AWS Certified SysOps Administrator Associate
• AWS Certified Developer Associate

AWS then offers professional-level exams, which require the candidates to have at least two years of experience with designing, operating, and troubleshooting the solutions using the AWS cloud. At the time of this writing, AWS offers two professional exams:

• AWS Certified Solutions Architect Professional
• AWS Certified DevOps Engineer Professional

AWS also offers specialty exams, which are considered to be professional-level exams and require deep technical expertise in the area being tested. At the time of this writing, AWS offers six specialty exams:

• AWS Certified Advanced Networking Specialty
• AWS Certified Security Specialty
• AWS Certified Alexa Skill Builder Specialty
• AWS Certified Database Specialty
• AWS Certified Data Analytics Specialty
• AWS Certified Machine Learning Specialty

You are preparing for the AWS Certified Data Analytics Specialty exam, which covers the services that are discussed in the book. However, this book is not the "bible" on the exam; this is a professional-level exam, which means you will have to bring your A game to the table if you are looking to pass the exam. You will need to have hands-on experience with data analytics in general and AWS analytics services in particular. In this introduction, we will look at what you need to do to prepare for the exam and how to sit for the actual exam and then provide you with a sample exam that you can attempt before you attend the actual exam.

Let's get started.

Registering for the Exam

You can schedule any AWS exam by following this link bit.ly/PrepareAWSExam. If you don't have an AWS certification account, you can sign up for the account during the exam registration process.

You can choose an appropriate test delivery vendor like Pearson VUE or PSI or proctor it online. Search for the exam code DSA-C01 to register for the exam.

At the time of this writing, the exam costs $300, with the practice exam costing $40. The cost of the exam is subject to change.

Studying for the Exam

While this book covers information around the data analytics landscape and the technologies covered in the exam, it alone is not enough for you to pass the exam; you need to have the required practical knowledge to go with it. As a recommended practice, you should complement the material from each chapter with practical exercises provided at the end of the chapter and tutorials on AWS documentation. Professional-level exams require hands-on knowledge with the concepts and tools that you are being tested on.

The following workshops are essential for you to go through before you can attempt the AWS Data Analytics Specialty exam. At the time of this writing, the following workshops were available to the general public, and each provides really good technical depth around the technologies:

• AWS DynamoDB Labs - amazon-dynamodb-labs.com
• Amazon Elasticsearch workshops - deh4m73phis7u.cloudfront.net/log-analytics/mainlab
• Amazon Redshift Modernization Workshop - github.com/aws-samples/amazon-redshift-modernize-dw
• Amazon Database Migration Workshop - github.com/aws-samples/amazon-aurora-database-migration-workshop-reinvent2019
• AWS DMS Workshop - dms-immersionday.workshop.aws
• AWS Glue Workshop - aws-glue.analytics.workshops.aws.dev/en
• Amazon Redshift Immersion Day - redshift-immersion.workshop.aws
• Amazon EMR with Service Catalog - s3.amazonaws.com/kenwalshtestad/cfn/public/sc/bootcamp/emrloft.html
• Amazon QuickSight Workshop - d3akduqkn9yexq.cloudfront.net
• Amazon Athena Workshop - athena-in-action.workshop.aws
• AWS Lakeformation Workshop - lakeformation.aworkshop.io
• Data Engineering 2.0 Workshop - aws-dataengineering-day.workshop.aws/en
• Data Ingestion and Processing Workshop - dataprocessing.wildrydes.com
• Incremental data processing on Amazon EMR - incremental-data-processing-on-amazonemr.workshop.aws/en
• Realtime Analytics and serverless datalake demos - demostore.cloud
• Serverless datalake workshop - github.com/aws-samples/amazon-serverless-datalake-workshop
• Voice-powered analytics - github.com/awslabs/voice-powered-analytics
• Amazon Managed Streaming for Kafka Workshop - github.com/awslabs/voice-powered-analytics
• AWS IOT Analytics Workshop - s3.amazonaws.com/iotareinvent18/Workshop.html
• Opendistro for Elasticsearch Workshop - reinvent.aesworkshops.com/opn302
• Data Migration (AWS Storage Gateway, AWS snowball, AWS DataSync) - reinvent2019-data-workshop.s3-website-us-east-1.amazonaws.com
• AWS Identity - Using Amazon Cognito for serverless consumer apps - serverless-idm.awssecworkshops.com
• Serverless data prep with AWS Glue - s3.amazonaws.com/ant313/ANT313.html
• AWS Step Functions -...