Build a Data Warehouse in AWS (Coursera)

Offered by Edureka,
Build a Data Warehouse in AWS (Coursera)

Embark on a transformative journey with our "Build a Data Warehouse in AWS" course, immersing yourself in the landscape of Amazon Redshift. This comprehensive course equips you with the essential skills not only to navigate but also to harness the full potential of this robust cloud-based data warehousing solution.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

During this course, you will explore the applications of AWS data warehousing solutions and delve into its fundamentals. By the end of this course, you will be able to:

  • Explore and understand the concepts of AWS Services used for Data Warehousing in AWS.
  • Gain proficiency in creating and managing IAM users and groups, understanding IAM policies and roles, and applying this knowledge in practical, hands-on scenarios.
  • Differentiate between traditional and cloud storage, operate Simple Storage Service, and understand various storage classes and versioning in AWS S3.
  • Gain a solid understanding of Amazon Redshift and effectively utilize it for data warehousing needs.
  • Effectively set up and managed a data warehouse using Amazon Redshift.
  • Develop a thorough understanding and practical expertise in implementing ETL (Extract, Transform, Load) processes in Amazon Redshift.
  • Acquire knowledge on monitoring and optimizing the performance of Amazon Redshift environments.

This course is designed for Data Engineers, Cloud Solution Architects, Data Analysts, Database Administrators (DBAs), Software Developers, and Engineers seeking to explore the AWS Data Warehouse and its applications.
Prior working experience with RDBMS, SQL, and AWS is recommended but optional for this course.
Begin an educational journey to learn about the Data Warehouse in AWS and its concepts for handling and analyzing large volumes of data.

What you'll learn
Working with AWS services, SQL, Data warehousing, Amazon Redshift, Amazon Redshift Serverless, Redshift Spectrum, and ETL in Amazon Redshift.

Syllabus

Introduction to AWS Services
Welcome to this module highlighting AWS essentials, encompassing the AWS Free Tier account, AWS Identity and Access Management (IAM), compute services, and storage services. Explore foundational aspects for account security, delve into IAM for access control, and discover compute and storage services for robust cloud solutions.

Empowering Data Warehousing using SQL and AWS
Welcome to this module, which focuses on AWS networking, databases, SQL overview, and data warehousing introduction. Explore AWS networking concepts, master database solutions on AWS, and grasp SQL fundamentals. Gain insights into data warehousing for efficient data management. Acquire skills to optimize AWS resources, ensuring robust data storage, protection, and sharing strategies.

Fundamentals of Amazon Redshift
Welcome to "Getting Started with Amazon Redshift", a comprehensive module designed for anyone interested in mastering Amazon Redshift, a powerful data warehousing solution. This module is structured in three main sections, each diving deep into various aspects of Amazon Redshift. In the first section, "Introduction to Amazon Redshift", we begin by exploring the foundational elements of Amazon Redshift, including its features, architecture, data processing flow, and the benefits it offers over other data warehousing solutions. Through a blend of theoretical knowledge and practical insights, this section provides a solid understanding of why organizations prefer Amazon Redshift for their data warehousing needs. Moving on to the second section, "Classification of Amazon Redshift", we will delve into the specifics of Amazon Redshift Serverless and provisioned clusters. Here, you'll learn about the architecture of both serverless and provisioned clusters, and understand their comparative advantages. This section includes real-world case studies, highlighting how different organizations effectively use Amazon Redshift Serverless and Provisioned Clusters in diverse scenarios. The final section, "Key Elements in Amazon Redshift", focuses on the technical components like clusters, nodes, serverless workgroups, and columnar storage. Special emphasis is placed on security aspects and managing users and groups within Amazon Redshift. We also discuss the advantages of columnar storage over traditional row-based storage, particularly in terms of query performance and data compression. This module is ideal for Data Professionals, Project Managers, Database Administrators, and Business Analysts looking to deepen their understanding of data warehousing with Amazon Redshift. Whether you are a fresher or have some experience with AWS services, this module will enhance your skills and knowledge, preparing you for practical application in real-world scenarios. By the end of this module, you will have a fundamental understanding of Amazon Redshift and its key elements. The journey culminates in a comprehensive assessment to consolidate your learning and proficiency in the subject. Embark on this educational journey to gain a thorough understanding of Amazon Redshift, equipping yourself with the skills to leverage this powerful tool in your data warehousing and analytics projects.

Build a Data Warehouse in Amazon Redshift
Welcome to the "Build a Data Warehouse in Amazon Redshift" module. This comprehensive program is meticulously crafted to guide you through the nuances of setting up and managing a data warehouse in Amazon Redshift. Throughout this journey, you will acquire hands-on skills in configuring AWS Secrets Manager, creating a Key Management Service (KMS) for Redshift Serverless, and understanding the intricacies of IAM Roles for Redshift Query Editor v2. The module is segmented into detailed parts, starting with creating a Data Warehouse in Amazon Redshift, followed by uploading and managing sample data in S3. You will delve into creating and loading tables in Query Editor v2 and learn to run and visualize sample queries. A significant portion of this module is dedicated to discussing the importance of KMS in the context of Redshift Serverless, focusing on its role in data encryption and security in a serverless environment. As you progress, you will explore Redshift Spectrum, understanding its dataset and pricing model, and develop strategies for optimizing query performance. This module also covers the ETL (Extract, Transform, and Load) process in Amazon Redshift, with a special emphasis on Zero-ETL integrations and their benefits. Monitoring in Amazon Redshift is another critical area you will master. This includes viewing cluster performance data, query history, database performance, and understanding the impact of workload concurrency. You will learn how monitoring individual queries and loads can significantly contribute to performance tuning and optimization. This module is ideal for individuals aspiring to specialize in data warehousing, such as Data Professionals, Project Managers, Database Administrators, and Business Analysts. It is particularly beneficial for those with a foundational understanding of AWS services, although prior experience is not mandatory. By the end of this module, you will have a deep understanding of building and managing a data warehouse in Amazon Redshift, equipped with practical knowledge and skills to optimize data storage, retrieval, and security effectively. The journey culminates in a comprehensive assessment to consolidate your learning and proficiency in the subject.

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Business intelligence and data warehousing (Coursera) Coursera
Universidad Nacional Autónoma de México

Business intelligence and data warehousing (Coursera)

Welcome to the specialization course Business Intelligence and Data Warehousing. This course will be completed on six weeks, it will be supported with videos and various documents that will allow you to learn in a very simple way how to identify, design and develop analytical information systems, such as Business Intelligence with a descriptive analysis on data warehouses. You will be able to understand the problem of integration and predictive analysis of high volume of unstructured data (big data) with data mining and the Hadoop framework.

Nov 2nd 2026
5-12 Weeks
Amazon Aurora Service Introduction (Coursera) Coursera
Edureka

Amazon Aurora Service Introduction (Coursera)

Welcome to the Amazon Aurora Service Introduction course, where you'll embark on a journey to acquire practical expertise in Aurora databases and harness the power of Amazon Web Services (AWS) for efficient data management. Throughout this short course, you'll explore the industry-specific applications of Aurora and delve into database fundamentals.

Nov 9th 2026
1 Week
ETL and Data Pipelines with Shell, Airflow and Kafka (Coursera) Coursera
IBM

ETL and Data Pipelines with Shell, Airflow and Kafka (Coursera)

After taking this course, you will be able to describe two different approaches to converting raw data into analytics-ready data. One approach is the Extract, Transform, Load (ETL) process. The other contrasting approach is the Extract, Load, and Transform (ELT) process. ETL processes apply to data warehouses and data marts. ELT processes apply to data lakes, where the data is transformed on demand by the requesting/calling application.

Nov 2nd 2026
5-12 Weeks
Introduction to Clinical Data Science (Coursera) Coursera
University of Colorado System

Introduction to Clinical Data Science (Coursera)

This course will prepare you to complete all parts of the Clinical Data Science Specialization. In this course you will learn how clinical data are generated, the format of these data, and the ethical and legal restrictions on these data. You will also learn enough SQL and R programming skills to be able to complete the entire Specialization - even if you are a beginner programmer. While you are taking this course you will have access to an actual clinical data set and a free, online computational environment for data science hosted by our Industry Partner Google Cloud.

Nov 2nd 2026
4 Weeks
Exam Prep DP-300: Azure Database Administrator Associate (Coursera) Coursera
Whizlabs

Exam Prep DP-300: Azure Database Administrator Associate (Coursera)

Database administration is one of the basic necessities of every enterprise. The growth of cloud and On-premises relational databases continues to create the need for professional database administrators. Therefore, an updated certification such as DP-300 could open up favorable options for employment of database administrators.

Nov 9th 2026
5-12 Weeks
Big Data Analysis with Scala and Spark (Coursera) Coursera
École Polytechnique Fédérale de Lausanne

Big Data Analysis with Scala and Spark (Coursera)

Manipulating big data distributed over a cluster using functional concepts is rampant in industry, and is arguably one of the first widespread industrial uses of functional ideas. This is evidenced by the popularity of MapReduce and Hadoop, and most recently Apache Spark, a fast, in-memory distributed collections framework written in Scala. In this course, we'll see how the data parallel paradigm can be extended to the distributed case, using Spark throughout.

Nov 9th 2026
4 Weeks
Data Governance with Databricks (Coursera) Coursera
Whizlabs

Data Governance with Databricks (Coursera)

Databricks is a cloud-based data engineering tool used to process and transform large amounts of data and explore the data through machine learning models. It combines data warehouses & data lakes into a lakehouse architecture. Data governance is a broad approach that comprises the principles, practices, and tools to manage an organization’s data assets throughout its lifecycle. A data governance strategy allows organizations to make data easily available protecting their data from unauthorized access, and ensuring compliance with regulatory requirements.

Nov 9th 2026
4 Weeks
Prepare Data for Exploration (Coursera) Coursera
Google

Prepare Data for Exploration (Coursera)

This is the third course in the Google Data Analytics Certificate. These courses will equip you with the skills needed to apply to introductory-level data analyst jobs. As you continue to build on your understanding of the topics from the first two courses, you’ll also be introduced to new topics that will help you gain practical data analytics skills. You’ll learn how to use tools like spreadsheets and SQL to extract and make use of the right data for your objectives and how to organize and protect your data. Current Google data analysts will continue to instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources.

Nov 2nd 2026
5-12 Weeks
Using Databases with Python (Coursera) Coursera
University of Michigan

Using Databases with Python (Coursera)

This course will introduce students to the basics of the Structured Query Language (SQL) as well as basic database design for storing data as part of a multi-step data gathering, analysis, and processing effort. The course will use SQLite3 as its database. We will also build web crawlers and multi-step data gathering and visualization processes. We will use the D3.js library to do basic data visualization.

Nov 9th 2026
5-12 Weeks
Building Web Applications in Django (Coursera) Coursera
University of Michigan

Building Web Applications in Django (Coursera)

In this course, you will learn how Django communicates with a database through model objects. You will explore Object-Relational Mapping (ORM) for database access and how Django models implement this pattern. We will review the Object-Oriented (OO) pattern in Python. You will learn basic Structured Query Language (SQL) and database modeling, including one-to-many and many-to-many relationships and how they work in both the SQL and Django models. You will learn how to use the Django console and scripts to work with your application objects interactively.

Nov 2nd 2026
4 Weeks