Databricks fundamentals
This Databricks Fundamentals course will help participants in getting a proper understanding of the internal structure and functioning of Databricks, the most powerful big data processing tool.
Master the fundamentals of data warehousing with our "Data Warehouse Fundamentals" course. Explore key concepts, architectures, and methodologies from Inmon, Kimball, and DataVault. Understand how data governance and design methods shape modern data warehouses. Ideal for those looking to build robust, scalable data systems.
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Data Warehouse Fundamentals is a comprehensive course designed to provide a solid understanding of data warehousing, from basic concepts to advanced methodologies. Whether you're new to data management or looking to deepen your expertise, this course offers a structured approach to learning the essential components and architectures that make up a modern data warehouse.
The course begins with an introduction to the concept of a data warehouse (DWH), its capabilities, limitations, and the business problems it addresses. You’ll gain insight into why organizations invest in DWHs and how they help transform data into actionable insights.
As you progress, you'll explore traditional and modern approaches to data warehouse design. This includes an overview of key components such as staging areas, Operational Data Stores (ODS), Data Marts, and Business Intelligence (BI) systems. You'll also learn about different design methodologies, including foundational concepts from Inmon, Kimball, and DataVault, which offer various perspectives on data warehouse architecture.
The course also covers critical aspects of data governance, highlighting the importance of managing data as a valuable asset. You'll delve into master data and master data management (MDM), understanding how to ensure data quality, consistency, and compliance across the enterprise.
In addition, the course focuses on the techniques involved in designing a data warehouse, from engaging stakeholders to determining the infrastructure needed to support a robust DWH environment. You’ll discuss the importance of the Initial Data Store Area (Stage), compare it with a Data Lake, and analyze common pitfalls in organizing this crucial area.
Later modules will examine the various layers of permanent data storage, including ODS and Data Delivery Systems (DDS). You'll explore ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes, which are critical for data retrieval, cleaning, and transformation into target storage systems.
In the final sections, the course explores how data warehouses integrate with data consumer systems, particularly BI applications. You'll gain insight into typical use cases for retrieving data from DWHs and the diverse range of BI systems available today.
Lastly, you’ll discuss the challenges of scaling data warehouses and how emerging trends such as machine learning and the Data Mesh concept are influencing the future of data warehousing.
By the end of this course, participants will:
This course primarily focuses on discussions of concepts and methodologies, offering limited hands-on practice. While it covers a wide range of problems and solutions related to data warehousing, the emphasis is on theoretical understanding and discussion rather than extensive practical exercises.
Upon completion of the "Data Warehouse Fundamentals" course, trainees will be able to:
Address scalability challenges and incorporate new technologies like Data Mesh and machine learning into DWH strategies.
This training will be useful for:
It can also be of interest for: