In the rapidly evolving landscape of data architecture, the data mesh architecture has emerged as a groundbreaking approach to solving the challenges of scaling and democratizing data within organizations. This presentation covers the core principles of data mesh and offers insights into how it stands apart from traditional data architectures such as data warehouses, data lakes, data fabric, and others. We will explain the key concepts of data mesh, including domain-oriented ownership, and self-serve data infrastructure, among others, and discuss how the decentralized nature of data mesh enables cross-functional teams to assume ownership of their data domains, while simultaneously enabling other teams to access and harness the full potential of that data, thus facilitating seamless analytics and data sharing.

Attendees will learn:

• Key principles of data mesh
• Differences between data mesh and other data architectures (data fabric, data lake, data mart, etc.)
• Data mesh best practices with real-world use cases

Speaker: Pragnya Gandhi

Pragnya Gandhi has over three decades of experience in everything data. She specializes in the architecture and design of complex systems and enterprise data platforms. Today, she supports multiple advanced analytics initiatives as a Senior Principal Architect at FINRA.

Speaker: Rakhi Bhatia

Rakhi Bhatia has over twenty years of experience in data.  This includes analysis, design, development, and architecture for transactional as well as analytical systems across multiple industries including capital markets, commercial real estate, healthcare, and retail. Rakhi is a Principal Data Architect at FINRA.