Data Warehouses are vital components of today’s businesses, providing a centralized repository for storing, managing, and analyzing large volumes of data. With the increasing popularity of online learning platforms, many individuals seek to expand their knowledge and skills in this area by enrolling in Data Warehouse courses online. In this article, we will explore some of the best online courses available for individuals interested in enhancing their Data Warehouse expertise. The courses selected have been carefully evaluated based on their content, credibility, and overall quality, ensuring that learners gain valuable knowledge and skills that can be applied to real-world scenarios.
Here’s a look at the Best Data Warehouse Courses and Certifications Online and what they have to offer for you!
Online Data Modeling Data Warehouse Course
- Online Data Modeling Data Warehouse Course
- 1. Data Warehouse Fundamentals for Beginners by Alan Simon (Udemy) (Our Best Pick)
- 2. Data Warehouse Developer-SQL Server/ETL/SSIS/SSAS/SSRS/T-SQL by Bluelime Learning Solutions (Udemy)
- 3. Data Warehouse Concepts: Basic to Advanced concepts by Sid Inf (Udemy)
- 4. Data Warehouse Development Process by Sid Inf (Udemy)
- 5. Implementing a Data Warehouse with SQL Server 2012 by Compaq learning (Udemy)
- 6. Modeling Data Warehouse with Data Vault 2.0 by Esra Ekiz (Udemy)
- 7. Data Engineering, Serverless ETL & BI on Amazon Cloud by Siddharth Raghunath (Udemy)
- 8. Data Warehouse – The Ultimate Guide by Nikolai Schuler (Udemy)
- 9. Learn Data Warehousing From Scratch- From Solution Architect by Asif Raza (Udemy)
- 10. ETL Framework for Data Warehouse Environments by Sid Inf (Udemy)
1. Data Warehouse Fundamentals for Beginners by Alan Simon (Udemy) (Our Best Pick)
This course titled Data Warehouse Fundamentals for Beginners provides practical techniques for IT professionals to plan, design, and build a data warehouse or data mart. The course includes sample data warehousing architectures and dimensional data structures which emphasize the best practices and techniques covered in the course. Each section has either scenario-based quiz questions or hands-on assignments that emphasize key learning objectives. The course instructor, Alan Simon, has over 30 years of experience in the data warehousing field and has written several books and articles on the subject. The course covers foundational data warehousing concepts, the relationship between data warehousing and business intelligence, co-existence with data lakes and data virtualization, architectural alternatives, dimensional analysis and modeling, relational database capabilities, handling changing data history, and designing ETL capabilities. Throughout the course, examples clearly demonstrate the key concepts and techniques covered. By the end of the course, students will be equipped to make key decisions required for data warehousing implementation. Data warehousing is both an art and a science, and the course emphasizes the fusion of both aspects to bring to students’ organizations and work. The course includes 10 sections which cover Welcome, Data Warehousing Concepts, Data Warehousing Architecture, Bring Data Into Your Data Warehouse, Data Warehousing Design: Building Blocks, Design Facts, Fact Tables, Dimensions, and Dimension Tables, Managing Data Warehouse History Through Slowly Changing Dimensions, Designing Your ETL, Selecting Your Data Warehouse Environment, and Conclusion.
2. Data Warehouse Developer-SQL Server/ETL/SSIS/SSAS/SSRS/T-SQL by Bluelime Learning Solutions (Udemy)
Bluelime Learning Solutions offers a course titled Data Warehouse Developer-SQL Server/ETL/SSIS/SSAS/SSRS/T-SQL. This course teaches students how to design and implement a data warehouse solution using Microsoft SQL Server. Students will also learn how to implement ETL with SQL Server Integration Services, as well as validate and cleanse data with SQL Server Data Quality Services and SQL Server Master Data Services.
The primary responsibilities of a data warehouse developer include implementing a data warehouse, developing SSIS packages for data extraction, transformation, and loading, enforcing data integrity using Master Data Services, and cleansing data using Data Quality Services.
Prerequisites for this course include experience working with relational databases, querying with Transact-SQL, basic programming constructs (such as looping and branching), and an awareness of key business priorities such as revenue, profitability, and financial accounting.
Throughout the course, students will learn how to deploy and configure SSIS packages, download and install SQL Server, attach the AdventureworksDW database, download and install SSDT, download and install Visual Studio, describe data warehouse concepts and architecture considerations, select an appropriate hardware platform, and design and implement a data warehouse. Additionally, students will learn how to implement data flow in an SSIS package, debug and troubleshoot SSIS packages, implement ETL solutions for incremental data extraction and loading, and implement data cleansing using Microsoft Data Quality Services.
Other topics covered include implementing Master Data Services, extending SSIS with custom scripts and components, and consuming data in a data warehouse. The course also covers Transact-SQL (T-SQL) basics, such as installing SQL Server, installing SSMS, creating databases and tables, creating views and stored procedures, reading and updating data, and backing up and restoring databases.
Overall, this course provides a comprehensive introduction to data warehouse development using SQL Server and related technologies.
The Data Warehouse Concepts course is designed to teach basic to advanced concepts in BI/Data Warehouse/BIG DATA. The course is instructed by Sid Inf and covers all the terminologies related to the Data Warehouse, such as OLTP, OLAP, Dimensions, Facts, and more. It also explains Start Schema, Snow flake Schema, and other options available along with their differences. The course covers managing data within the Data Warehouse and explains the process of reading and writing data onto the Warehouse.
The course also covers the basics of Data Modelling and teaches how to start with it logically and physically. Concepts related to Facts, Dimensions, Aggregations, and commonly used techniques of ETL are also explained. Upon completion of the course, learners will have a clear understanding of all the concepts related to the Data Warehouse, which can help them start off with the next step of becoming an ETL developer or administering the Data warehouse environment with the help of various tools.
The course is divided into various sections including Introduction, Business Intelligence, Data Warehouse Architectures, ODS – Operational Data Store, OLAP, Data Mart, Metadata, Data Modeling, Entity Relational Data Model, Dimensional Model, DWH Indexes, Data Integration and ETL, ETL Vs ELT, Typical Roles In DWH Project, and DW/BI/ETL Implementation Approach. The course also includes retired lectures.
Overall, the Data Warehouse Concepts course is an extensive and detailed course that teaches learners all the essential concepts related to the Data Warehouse, BI, and Data Modelling. The course is suitable for beginners who want to learn the basics and for professionals who want to enhance their skills and knowledge.
The Data Warehouse Development Process course focuses on the various challenges faced during the development of an Enterprise Data Warehouse. These challenges include complex business rules, different development methods, regulatory requirements, and compliance from third party governing bodies. The course emphasizes practical situations and best practices for sustainable, scalable, and robust implementations.
The course is divided into several sections, including an introduction, different categories of Data Warehouse implementations, keywords/terminology, methodologies used for Data Warehouse software development, and various SDLC models such as Waterfall, V Model, Agile, Incremental, and Iterative. The course also covers practical implementation steps such as project planning, requirements gathering, design, development, deployment, maintenance, and operations.
The course provides a comprehensive review of completed tasks and production support and maintenance, and concludes with a final note. Overall, the Data Warehouse Development Process course is designed to help developers handle real-time practical situations more effectively while building a sustainable and robust Enterprise Data Warehouse.
The course Implementing a Data Warehouse with SQL Server 2012 is offered by Compaq Learning and targets data professionals, including data analysts and aspiring professionals, who want to prepare for exam 70-463. The course covers the construction and usage of databases in SQL Server 2012, and is a step higher into the administration of the data system.
The course aims to provide a thorough understanding of data warehouses and dimensions, the importance of Fact Table, and the concepts involved in implementing Data Warehouse with SQL Server 2012. It also covers the different elements of Control Flow and the use of variables. Participants will learn about the different types of Transforms available in SSIS, how to deploy and manage packages, and how to debug and secure packages.
This course is considered the basis for all other SQL Server-related disciplines, including Database Development, Database Administration, and Business Intelligence. It is designed to help students comprehend SQL Server 2012 database administration, including installation and configuration. Exam 70-463 is part of a series of certifications to master this platform.
In addition to building and managing data warehouses and implementing dimensions, the course addresses important topics such as instance, database, and object security strategies, implementing and automating ETL solutions, and high availability technologies. The training consists of 90 lecture sessions and includes demos on major concepts to help participants understand how the steps learned are implemented in real time.
The course is broken down into several sections, including an introduction to data warehouses, dimension tables, fact tables, and concepts involved in implementing a data warehouse with SQL Server 2012. It also covers SSIS overview for ETL, understanding SSIS Control Flow, working with variables, deploying and managing packages, and debugging and securing packages.
This course, titled Modeling Data Warehouse with Data Vault 2.0, is instructed by Esra Ekiz and covers the basics and fundamentals of Data Vault 2.0, along with Agile Methodology and Big Data. The Data Vault approach is designed to simplify data integration from multiple sources and to offer auditability and design flexibility in handling data from the heterogeneous information systems commonly used in businesses today. It is designed to deliver an Enterprise Data Warehouse while solving many of the drawbacks of the 3NF (Inmon) and Dimensional Modelling(Kimball).
The course covers a range of topics, including Data Modelling, traditional Data Warehouse approaches, and the issues and problems associated with 3NF or Star Schema. It also delves into how Data Vault addresses these challenges and provides an innovative approach. The course covers the fundamentals of the Data Vault modeling approach, including Hubs, Links, and Satellites, as well as the different architectural and modeling layers of Data Vault 2.0. The Business Vault, Information Vault, and significance of the Dimensional Layer are also covered.
Participants will learn about loading patterns and architecture, how to handle schema and grain changes on the Data Vault model, and the importance of Agile Methodology for scalable Data Warehouses. The course also covers Big Data Terminologies along with Data Vault Methodology. A hands-on case study is included to help participants understand the principles and concepts.
Overall, this course provides an in-depth overview of the Data Vault 2.0 approach, as well as related topics such as Agile Methodology and Big Data. Participants will walk away with an understanding of the fundamentals of data modeling, traditional Data Warehouse approaches, and how Data Vault addresses many of the challenges associated with those approaches.
This course, titled Data Engineering, Serverless ETL & BI on Amazon Cloud, is instructed by Siddharth Raghunath. It focuses on data warehousing and ETL on the AWS cloud, covering the end-to-end lifecycle of a typical data engineering project. The course is designed for anyone who wants hands-on expertise in setting up a data warehouse in Redshift or setting up a BI infrastructure from scratch. It is particularly useful for data scientists, analysts, and business analysts who are expected to handle the technical aspects of data ingestion, engineering, and warehousing.
The course covers setting up a data warehouse in AWS Redshift from scratch, basic data warehousing concepts, writing serverless AWS Glue jobs for ETL and batch processing, using AWS Athena for ad-hoc analysis, using AWS Data Pipeline to sync incremental data, and setting up Lambda functions to trigger and automate ETL/data syncing processes. It also includes QuickSight setup, analyses, and dashboards.
Prerequisites for this course include a basic understanding of how the cloud works, Python/SQL (an absolute must), and knowledge of how to write some basic PySpark scripts. An active AWS account is also required. The course makes use of the free tiers for Redshift and RDS, and AWS UI on the browser for creating clusters and setting up jobs. There is no bash scripting involved, and any operating system can be used for lab sessions.
This course is not code-heavy, with only 35% coding involved. Its purpose is to make everyone aware of and comfortable with all the tools and features used in this course. Some tips for getting the most out of the course include watching the videos at 1.2x speed, researching other tools meant for the same purpose as each component or feature, and putting in extra effort to succeed.
The course titled Data Warehouse – The Ultimate Guide is designed to teach individuals how to implement a data warehouse in a modern way. The course is led by Nikolai Schuler, an expert in data modeling and warehousing. The course offers 9 hours of video lectures and covers both the theory and practice of data warehousing. Students will learn how to set up an ETL process, implement dimensional modeling, and optimize a data warehouse using indexes.
The course is the most comprehensive and modern offering on data warehousing available. Students will learn everything from the basics to the advanced topics in a step-by-step manner. The course offers hands-on demonstrations, assignments, and quizzes to master the concepts. By the end of the course, students will have the practical skills, knowledge, and confidence to implement a modern data warehouse professionally.
The course covers topics such as Data Warehouse Basics, Data Warehouse Architecture, Dimensional Modeling, Slowly Changing Dimensions, ETL process, ETL tools, and Optimizing a Data Warehouse using indexes. The course also offers advanced topics such as Columnar Storage, OLAP Cubes, In-memory databases, massive parallel processing, and cloud data warehouses. Moreover, students will learn the differences between ETL and ELT processes, and how to practically use and connect a data warehouse.
The course is ideal for individuals who wish to upskill their career in Business Intelligence & Data Engineering. Students will learn how to architect and implement a data warehouse in a professional manner. It is suitable for data architects, data engineers, data analysts, or Business Intelligence experts. The course offers lifetime access, and students can avail a 30-day money-back guarantee if they are not satisfied with the course.
The course is divided into several sections, including Intro, Data Warehouse Basics, Data Warehouse Architecture, Dimensional Modeling, Slowly Changing Dimensions, ETL process, ETL tools, Case Study: Creating a Data Warehouse, ETL vs.
The course titled Learn Data Warehousing From Scratch- From Solution Architect is instructed by Asif Raza, an industry expert with experience in several data warehousing implementation projects in the UK. The course aims to provide learners with the essentials needed to implement a successful data warehouse project and eventually become an expert in the business intelligence domain. The course covers topics such as the business challenge and need for business intelligence, defining data warehouse, industry usage of data warehousing, and typical BI environments. It also delves into data warehousing concepts such as OLTP, OLAP, ODS, data marts, ETL, facts, dimensions, SCD, surrogate keys, and factless-fact. Two major schools of thought on data warehouse design, Ralph Kimball and Bill Inmon, are discussed along with their respective approaches and sample data models. Data warehouse appliances like Teradata, Netezza, and Exadata are also covered. The course also addresses big data and its importance in the BI world, major players, Hadoop in the DW world, and an example architecture. NoSQL databases are explored, including their types and differences from SQL. The course includes a quiz and reference materials with links/steps. Additionally, a Hadoop distributions comparison sheet is provided to help learners choose the appropriate distribution based on several parameters.
The course ETL Framework for Data Warehouse Environments is designed to provide a practical approach to implement an ETL (extract, transform and load) framework in typical Data Warehouse environments. The course covers the guidelines, standards, developer/architect checklist, and benefits of reusable code along with best practices and standards for implementing ETL solutions. The course can be incorporated to any ETL tool in the market, including Informatica 10x, Oracle 11g, IBM DataStage, Pentaho, Talend, and Ab-intio. The course includes multiple reusable code bundles from the marketplace, checklists, and the material required to get started on UNIX for basic commands and Shell Scripting.
The course content is divided into sections, which includes Getting Started, Metadata Categories, ETL Framework- Process Flow, Data Sourcing, Data Sourcing – Classification, Script Requirements for Data Sourcing, File Validation, The Staging Layer, Business Validation Layer, DataWarehouse Layer, Exception Handling/Error Handling, Project Setup, Extending the Operational Metadata’s Data Model, Error Handling Data Model, Mapping examples, Audit, Balance and Control, and Configuration Management.
The course is suitable for those who require a high-level approach to implement an ETL framework in any Data Warehouse environment. The practical approaches can be used to design and implement an ETL solution that is highly reusable with different data loading strategies, error/exception handling, audit balance and control handling, job scheduling, and restartability features. The course is also beneficial for those who have an existing ETL implementation and need to embed the ETL framework into the existing environment, jobs, and business requirements. The course may also require redesigning the whole mapping/mapplets and the workflows (ETL jobs) from scratch to improve design standards and reusability.