The Evolution of the Modern Data Platform
Key Takeaways
Our Six Steps
- Data as a product: build and manage data product outputs, which are a collection of data assets.
- Data marketplace: add a layer to trade data products. Adding this will clearly show integrity across data products, adding confidence to domains.
- Domain-level architecture: focus more on building data solutions for each domain which will add value per domain. Different domains are more or less and as complicated as one another.
- Front-to-back design: leading on from domain-level architecture, put the end consumer first. How are they going to use the data?
- Metadata management: management of metadata produced through all these products is critical to track not only redundant data, but to govern the landscape.
- Enhanced stream-driven: remove constant dependency on end-of-day processing when streams are available. Enhance the platform by supplying up to-date data so that information value may be gained sooner.
FAQ: Modern Data Platforms
What are the key components of a modern data platform blueprint?
A modern data platform includes three core layers: systems of record, systems of storage and processing, and systems of engagement. Data flows through raw/landing, curated, and distributed layers, each designed for domain‑specific needs.
Capabilities include data marketplaces, data fabrics, auto‑cataloging, and MLOps/DataOps pipelines to automate lineage, validation, and model retraining.
Explore the full blueprint and design considerations in the Thought Leadership report.
How has the data platform evolved from data warehouses to lakehouses?
Data platforms evolved from rigid relational data warehouses to flexible big data systems and later to lakehouses that combine structured optimization with unstructured agility. Data warehouses solved duplication and integrity issues, but were slow to scale. Big data enabled fast ingestion without modeling, but sacrificed optimization. Lakehouses aimed to bridge both worlds using metadata caching and architectural enhancements.
For a full evolution timeline and insights on where platforms are headed next, download the Thought Leadership.
How can organizations transition toward a modern data platform?
Transitioning requires adopting an MVP‑driven approach: assess domain needs, transform with modern data capabilities, manage governance, and monetize insights. This ATMM model ensures teams don’t “boil the ocean,” but instead deliver incremental value.
The modern approach blends existing technologies - data lakes, warehouses, lakehouses - into domain‑focused solutions, guided by business value rather than technical uniformity.
Download the full report for step‑by‑step transition guidance.
