Hello and welcome to this learning path, which has been designed to help you prepare and pass the AWS Certified Data Analytics - Specialty exam

Throughout this learning path, you will be guided via our courses, hands-on labs including some lab challenges, webinars, and a preparation exam at the end, all of which are focused on areas that will be assessed within the exam.  

As defined in the exam guide, which can be found here, the exam has been designed for individuals who perform in a data analytics-focused role. This exam validates an examinee’s comprehensive understanding of using AWS services to design, build, secure, and maintain analytics solutions that provide insight from data.

The questions within the exam are multiple choice requiring you to select either a single or multiple answers for each question.  The scoring is based out of 1000, with a minimum passing score of 750 (75%).

The exam is split into 5 different domains that you will be assessed against, each carrying a different percentage weighting, these are identified as:

• Domain 1: Collection 18% 
• Domain 2: Storage and Data Management 22% 
• Domain 3: Processing 24% 
• Domain 4: Analysis and Visualization 18%
• Domain 5: Security 18%

Across each of these domains we shall cover a number of different AWS services, features, methodologies, and processes.  By the end of this learning path, you would have gained the knowledge and understanding required to sit and take the certification exam.  

Let’s take a high-level look at just some of the services and components that you will be introduced to throughout this learning path.  

• Amazon S3: Gain an understanding of Amazon S3, including an insight into the different storage classes offered.  We shall also cover configurable bucket level features to help you manage and administer your data effectively, such as versioning, server access logging, default encryption, and transfer acceleration.
• Amazon EMR - Understand the characteristics of Amazon EMR and it’s core architecture such as Master, Core and Task nodes and the roles they play, in addition to its storage options HDFS, EMRFS, and the Local File System.  
• Amazon Redshift - You’ll be introduced to Redshift, the fast, fully-managed, petabyte-scale data warehouse, designed for high performance and analysis of information.  Learn the architectural components, understanding the difference between compute nodes, node slices, leader nodes, and more! 
• Amazon Athena - Gain in an in-depth review of Amazon Athena, explaining the fundamental storage and querying concepts. We will highlight suitable use cases in which Athena can be applied effectively, before spending time discussing the process of creating and setting up Athena databases, tables, and partitions. We examine the process in which Athena SQL queries are authored and how they are managed.
• AWS Glue - During our content focused on Glue, you will become familiar with Serverless Extract, Transform and Load, known as ETL.  Also, we’ll discuss the architecture of a typical ETL project.  The prerequisite setup of AWS to use AWS Glue for ETL.  Knowledge of how to use AWS Glue to perform serverless ETL, and how to edit ETL processes created from AWS Glue.
• Amazon Kinesis Family - Understand the key differences between the four services that make up the Amazon Kinesis family, these being, Kinesis Data Streams, Kinesis Video Streams, Kinesis Firehose, and Kinesis Analytics.  You will be able to explain how Amazon Kinesis makes it easy to collect, process, and analyze real-time streaming data so you can get timely insights and react quickly to new information.
• Amazon RDS - Discover the different DB engines RDS has to offer, in addition to the instance types such as general purpose and memory-optimized, providing varied performance.  You’ll also learn how to manage high availability, compute scaling, how to configure storage scaling for use with Provisioned IOPS, automated backup features, and more!
• Amazon DynamoDB - Gain a clear understanding of Amazon DynamoDB, designed to be used for ultra high performance and maintained at any scale with single-digit latency.  Learn the differences between on-demand and provisioned throughput, and how your read and write capacity units affects the performance of your tables.
• AWS Lambda - Learn how to use Lambda for automation through a dive of its components, including an explanation of the different elements of Lambda functions.  You’ll understand the key differences between policies used within Lambda and recognize how event sources and event mappings are managed for both synchronous and asynchronous invocations.
• Amazon Quicksight - Discover how to take your data used for analytics, and turn it into meaningful visualizations using a variety of different graphs and charts. Understand how visualization can help to drive business decisions, identify important trends, and highlight data relationships.  We shall cover topics such as data sources, data preparation, and the benefits of SPICE.

In addition to diving into different AWS services, you will also gain valuable knowledge surrounding data analytic concepts, including an understanding of:

• Data types, such as structured, semi-structured, and unstructured data
• Different types of analytics, such as batch, real-time and predictive analytics
• And the process behind running analytics against your data

From a visualization perspective, we will also cover how to display data relationships, data comparisons, data distributions, and data compositions through a variety of different chart types depending on your use case.

With so much content to get through, let’s begin! And if you have any questions throughout, please don’t hesitate to reach out to us by contacting support@cloudacademy.com