AWS: Data Analytics

Overview

AWS: Data Analytics is the fourth course of Exam Prep (DEA-C01): AWS Certified Data Engineer - Associate Specialization. This course assists learners in configuring data integration services to discover, move, and integrate data from multiple sources for application development. Learners will explore a serverless, interactive analytics service to analyze petabytes of data in AWS. This course teaches learners to extract data from various sources using big data frameworks such as Apache Spark, Hive, or Presto. The course is divided into two modules and each module is further segmented by Lessons and Video Lectures. This course facilitates learners with approximately 3:00-3:30 Hours of Video lectures that provide both Theory and Hands-On knowledge. Also, Graded and Ungraded Quizzes are provided with every module to test the ability of learners. Module 1: Data Integration in AWS Module 2: Data Analytics and ML in AWS By the end of this course, a learner will be able to: - Examine data integration services to integrate data from multiple sources for analytics and application development. - Centrally manage data lake access permissions and share data within and outside your organization. - Describe a fully managed service to process and analyze streaming data at any scale in AWS. This course is intended for candidates who wish to enhance their skills in analyzing large and complex datasets and have basic hands-on experience in analytics and database services.

Syllabus

Data Integration in AWS

Welcome to Week 1 of the AWS: Data Analytics course. This week, you will be introduced to AWS Glue, a fully managed ETL service for customers to prepare and load their data for analytics. You will explore some basic components of AWS Glue such as AWS Glue Catalog, Crawlers, Classifiers, etc. By the end of the week, you will learn some advanced features of AWS Glue Data Quality and AWS Glue DataBrew.

Amazon Athena and Amazon EMR in AWS

Welcome to Week 2 of the AWS: Data Analytics course. This week, you will be introduced to Amazon Athena, an interactive analytics service built on open-source frameworks, that provides a simplified way to analyze petabytes of data where it lives. You will also learn Amazon EMR, a managed cluster platform that simplifies running big data frameworks, such as Apache Hadoop and Apache Spark. By the end of the week, you will perform data integration using Amazon EMR and AWS Glue.

Data Analytics and ML in AWS

Welcome to Week 3 of the AWS: Data Analytics course. This week, you will be introduced to Data Analytics and ML services in AWS. You will learn Amazon Kinesis, a fully managed service to process and analyze streaming data at scale. You will explore Amazon Managed Service for Apache Flink to transform and analyze streaming data in real-time using Apache Flink. With Amazon QuickSight, one can enhance data-driven organizations with unified business intelligence (BI) at scale. By the end of this week, you will learn Amazon SageMaker, a fully managed service that can help to build, train, and deploy ML models at scale using a single integrated development environment (IDE).