Overview

This course provides essential skills for modernizing data architectures by adopting a data-as-a-product approach. It emphasizes building modular, scalable systems to enhance data management and unlock greater value from data assets. Throughout the course, you will learn to design, implement, and manage data products in a distributed environment, ensuring sustainable growth and scalability. By focusing on practical, real-world applications, the course empowers learners to overcome the challenges of traditional monolithic data platforms. What sets this course apart is its combination of theory and hands-on activities. You will not only grasp key concepts but also develop actionable strategies to modernize your data architecture effectively. Real-world case studies and best practices will guide you through the process. This course is ideal for data engineers, architects, and leaders looking to enhance their data strategies and modernize their organization’s data management. A solid understanding of core data engineering principles is recommended. This course is based on material written by an expert author, bringing the depth of a book into a more engaging, interactive format. The core content is delivered through clear, structured text you can read at your own pace, supported by short videos and quizzes to highlight key ideas and test your understanding. By combining the strengths of book learning with interactive assessments, you get the best of both worlds: the depth and clarity of an author’s expertise, plus the flexibility to revisit, practice, and reinforce concepts whenever you need.

Syllabus

From Data as a Byproduct to Data as a Product

In this section, we examine monolithic data platforms' limitations and explore modular approaches to reduce accidental complexity and improve system sustainability through data product strategies.

Data Products

In this section, we define data products and pure data products, analyze their structure and characteristics, and explore methods for classifying them to enhance data utilization.

Data Product-Centered Architectures

In this section, we examine data-product-centered architecture design, operational and management planes, and alternative data management approaches for scalable, adaptable solutions.

Identifying Data Products and Prioritizing Developments

In this section, we explore domain-driven design and event storming to identify data products, and manage their portfolio for business alignment and operational coherence.

Designing and Implementing Data Products

In this section, we examine data product design, interactions, and implementation, focusing on metadata management, machine-readable descriptors, and data flow processes for scalable solutions.

Operating Data Products in Production

In this section, we explore deploying data products using CI/CD, governing them with policies, and managing their evolution to ensure reliability and alignment with organizational standards.

Automating Data Product Lifecycle Management

This section covers automating data product lifecycle management with XOps, focusing on platform capabilities and implementation strategies.

Moving through the Adoption Journey

In this section, we explore the adoption phases of data as a product, focusing on challenges, strategies, and adaptive data governance to ensure sustainable and scalable implementation.

Team Topologies and Data Ownership at Scale

In this section, we explore team topologies and data ownership strategies to optimize organizational structures for scalable data product delivery and efficient team collaboration.

Distributed Data Modeling

In this section, we explore distributed data modeling techniques, focusing on physical and conceptual approaches to design explicit models for data products in decentralized systems.

Building an AI-Ready Information Architecture

In this section, we explore building an AI-ready information architecture by designing a knowledge plane for semantic interoperability and constructing an enterprise knowledge graph for AI integration.