Level: Beginner to Intermediate
Prerequisite: None
The data architecture landscape is filled with buzzwords and competing vendor claims, each promising to be the ultimate solution. Most of what is promoted as architecture blurs the lines between patterns, technologies, implementation, and fads. There are more choices than the data warehouse vs lakehouse question, or the central vs distributed decision. In reality, organizations rely on multiple patterns that exist in a data ecosystem.
This course focuses on understanding patterns—the fundamental building blocks of modern data architectures. Through a brief history of data systems from recent decades, we will clarify and categorize the most common architectural patterns. We'll discuss their strengths, limitations, and why multiple patterns exist.
Participants will gain a solid understanding of the real-world implications of various data architecture patterns, from embedded machine learning to self-service BI. We’ll explore how these patterns influence technology choices and team structures, helping you to better determine what to change and make informed decisions about what to build.
By the end of this workshop, students will have a clear understanding of the various architectural patterns used in modern data platforms, how to match them to specific business needs, and how to recognize the strengths and limitations of each. You will leave equipped with the knowledge to design resilient, efficient, scalable data systems that fit your organization's unique needs.
You Will Learn
- How to modernize data architecture by identifying the specific patterns you need
- Common architecture concepts to help you build better solutions
- A more complete set of patterns for different kinds of data and analytics applications
- The strengths and weaknesses of patterns being promoted today
- Gain a working understanding of architectural styles and architectural patterns
- How governance (data, analytics, AI) relates to architecture patterns
- How to evaluate and choose the optimal patterns for your organization's needs
Geared To
- Architects and designers of data systems
- Managers of analytics and data organizations
- Data engineers and application developers