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Data Warehousing Essentials for Analytics and AI Support

This course will cover various topics in Data Engineering in support of decision support systems, data analytics, data mining, machine learning, and artificial intelligence. You will study on-premises data warehouse architecture, and dimensional modeling of data warehouses.

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What's inside

Syllabus

Database Concepts 1

This module introduces data warehousing and business intelligence, emphasizing their role in enhancing organizational decision-making. Data warehouses transform raw data into actionable insights using processes like ETL (Extract, Transform, Load), supported by tools such as OLAP for querying and data mining. While operational databases (OLTP) are suited for daily transactions, OLAP databases are optimized for complex analytics. To effectively implement data warehousing solutions, it is essential to understand the underlying database design principles. Therefore, the module reviews key concepts related to operational databases, focusing on conceptual database design. We examine Entity Relationship Diagrams (ERD) as a vital tool for conceptual representation, identifying crucial aspects of the database design process that convert business requirements into a conceptual model. In the subsequent module, we will build on this foundation by reviewing logical modeling and the implementation of databases, equipping students with a comprehensive understanding of both the database design process and OLAP systems. This knowledge will serve as a stepping stone as we explore the complexities of data warehouses.

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Covers topics in data engineering that are highly relevant to industry

Explores multidimensional data modeling, which is a core skill in data warehousing

Develops foundational knowledge in database concepts, which serves as a stepping stone for understanding data engineering

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Reviews summary

Core data warehousing for analytics & ai

According to students, this course offers a solid foundational understanding of data warehousing essentials, particularly for those looking to support analytics and AI initiatives. Many learners highlight the course's ability to bridge theoretical database concepts with practical data warehouse design, making it highly relevant for working professionals. The instructor's engaging approach and clear explanations are frequently praised, contributing to a largely positive learning experience. While generally well-received, a few students noted that the initial database concepts modules might be a review for those with prior SQL knowledge, and some express a desire for more advanced or hands-on content.

Initial content may be basic for those with strong SQL skills.

"Found some of the database concepts modules a bit slow if you already knew SQL, but useful as a refresher."

"If you have a strong background in SQL and relational databases, the first two modules might feel a bit redundant."

"For experienced data professionals, the foundational database parts could be reviewed quickly to get to the core DW material."

Recent reviews indicate the course has improved over time.

"Comparing it to earlier feedback I saw, the course seems to have been updated to be more practical and relevant."

"The recent modules felt very current, suggesting the course creators are actively refining the content."

"I think the course has evolved well, addressing some of the earlier criticisms about its practical application."

Instructor delivers content clearly and keeps learners engaged.

"The instructor was very engaging and made complex topics easy to understand, which made a huge difference."

"Lectures were clear and concise, and the instructor's enthusiasm was contagious, making learning enjoyable."

"I found the explanations by the instructor to be very clear, helping me grasp difficult data warehousing concepts effectively."

Helps apply theory to real-world data warehousing.

"This course really helped me bridge the gap between database theory and practical data warehouse design for analytics."

"I appreciated how the course focused on the application of data warehousing for analytics and AI support, which is directly relevant to my work."

"The content felt highly relevant to real-world scenarios, which is crucial for professionals in data engineering."

Provides a robust understanding of core data warehousing.

"This course provided an excellent foundation in data warehousing, really solidifying my understanding of dimensional modeling and OLAP concepts."

"I gained a solid foundation from completing this course, especially in understanding facts, measures, and dimensions."

"The initial modules on database concepts and later on data warehouse principles were very well-structured for building a strong understanding."

Quizzes require careful attention and deep understanding.

"The course content is solid, but the quizzes were sometimes tricky and didn't always reflect lectures directly."

"Some quizzes felt quite challenging, requiring a thorough understanding rather than just surface-level recall."

"I had to spend extra time reviewing for the quizzes as they often tested very specific details."

Some learners seek more in-depth or hands-on practice.

"Could use more in-depth coverage on complex topics or optimization techniques in modern data warehouses."

"While excellent for foundation, I would have liked more hands-on labs or case studies to apply the concepts."

"I was hoping for more practical implementation examples beyond just the theoretical framework."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Warehousing Essentials for Analytics and AI Support with these activities:

Read 'Data Warehousing Fundamentals' by Ralph Kimball

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Gain a comprehensive understanding of data warehousing principles and best practices by reviewing this seminal text.

View The Kimball Group Reader on Amazon

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Read and understand the key concepts presented in the book, such as data warehouse architecture, data modeling, and data integration.
Consider the real-world examples and case studies provided in the book to enhance your practical understanding.

Review Database Basics

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Warm up your understanding of the fundamental principles of database design, ensuring a smooth transition as you delve into the complexities of data warehousing.

Browse courses on Database Design

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Revise key concepts of database design, focusing on conceptual modeling using Entity-Relationship Diagrams (ERDs).
Review the principles of normalization, aiming to achieve the 3rd Normal Form.

Hands-on SQL Exercises

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Reinforce your understanding of SQL by practicing various queries. This will enhance your ability to effectively retrieve and manipulate data.

Browse courses on SQL

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Execute SELECT queries to retrieve specific data from tables.
Utilize WHERE, ORDER BY, and JOIN clauses to refine your queries and retrieve targeted results.
Practice using functions and aggregates (COUNT, SUM, AVG, MIN, MAX) to summarize and analyze data.

Three other activities

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Design a Conceptual Data Model

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Solidify your grasp of conceptual data modeling by creating your own ERD. This will provide you with a deeper understanding of how to translate business requirements into a logical representation.

Browse courses on Data Modeling

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Identify the entities and relationships present in a given business scenario.
Construct an ERD that accurately represents the identified entities and their relationships.
Validate your ERD by ensuring it adheres to the principles of conceptual modeling.

Attend a Data Warehousing Workshop

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Enhance your practical knowledge and skills by attending a data warehousing workshop. This immersive experience will provide you with hands-on exposure to industry-leading practices and techniques.

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Identify and register for a reputable data warehousing workshop.
Actively participate in the workshop, asking questions and engaging in discussions with experts.
Apply the knowledge and techniques learned in the workshop to your own data warehousing projects.

Build a Data Warehouse Prototype

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Demonstrate your mastery of data warehousing concepts by building a prototype of a data warehouse. This hands-on project will challenge you to apply your knowledge and skills to a real-world scenario.

Browse courses on Data Warehouse Design

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Design the architecture of your data warehouse, including data sources, data transformation processes, and data storage.
Implement your data warehouse design using a suitable data warehousing tool.
Integrate data from multiple sources into your data warehouse.
Perform data analysis and reporting using your data warehouse.

Career center

Learners who complete Data Warehousing Essentials for Analytics and AI Support will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Data Warehousing Essentials for Analytics and AI Support.

The Data Warehouse ETL Toolkit

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Is specifically dedicated to the ETL process within a data warehousing context. It provides practical techniques and best practices for extracting, cleaning, transforming, and loading data. It's an excellent resource for understanding the intricacies of building robust ETL systems and is considered a key text for ETL developers and architects. This book is highly valuable as a reference tool for practitioners.

Data Warehousing Essentials for Analytics and AI Support

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What's inside

Syllabus

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Reviews summary

Core data warehousing for analytics & ai

Activities

Career center

Reading list

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