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Thu, April 23DataTech Architecture
Enterprises often struggle to manage data consistently across multiple systems, leading to configuration drift and regulatory risk.
This session presents a model driven data architecture where business domain model is elevated to a system agnostic, executable specification. Legend, an open source platform, enables this approach through a pure logical modeling language and extensible, declarative annotations as first class constructs, allowing teams to encode governance, security, and behavioral policies directly into the model at design time. These enriched models are compiled by a policy as code translation engine into platform specific artifacts ensuring deterministic and repeatable enforcement across technologies. The model derived design acts as an AI enabler — generating precise machine-readable semantics from a single model, reducing hallucination risk and enabling trustworthy automation.
What You Will Learn
Logical versus physical modeling and why separation is critical for durable data assets
How governance, security, and policies can be embedded directly into logical data models
How compiling models into platform-specific artifacts eliminates configuration drift and improves trust
Who Should Attend
Data architects
Enterprise architects
Platform engineers
Developers working on data-intensive systems
Technology leaders responsible for data governance and compliance
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Sumit Rastogi is a Tech Fellow and Vice President at Goldman Sachs, with 23 years of experience in engineering within the banking industry. His career began with a decade as a banking consultant, providing him with a broad perspective on the technological challenges and opportunities in the financial world. Since joining Goldman Sachs, Sumit has focused on building and scaling critical data infrastructure, including data platforms, reporting platforms, big data compute platforms and ETL products. A recognized innovator, Sumit has filed two patents for his work in data and reporting platforms. Sumit is passionate about leveraging technology to drive efficiency and innovation in the financial services sector.
Deepika is a Vice President at Goldman Sachs with 10 years of experience architecting mission-critical data infrastructure. A career-long engineer at the firm, she specializes in designing scalable data platforms and robust data pipelines that power the firm's global people data ecosystem. Deepika is a proponent of tech-agnostic architecture, building modular solutions and generic frameworks that allow for seamless data migration and interoperability across diverse technology stacks. Her expertise in sophisticated data modeling and her commitment to high-impact automation have consistently reduced operational costs and manual overhead.
