Federated IT, Not Platform Wars: Rethinking the Snowflake vs. Databricks Debate

Snowflake and Databricks have long been the focal points of enterprise data strategy discussions. Both offer robust ecosystems supporting analytics, AI, and data engineering. However, organizations often struggle with the rigidity of migrating or transforming large data volumes into platform-specific formats, raising concerns about vendor lock-in and inflexibility.

Recognizing this, Snowflake has added support for Apache Iceberg, enabling external storage while retaining the power of its analytics engine. Databricks has introduced UniForm, which allows Delta Lake tables to be accessed as Iceberg or Apache Hudi tables. While these moves aim to promote openness, they don't fully resolve the fragmented nature of modern data architecture.

The Reality of Enterprise Data: It’s Everywhere

Data isn't confined to a single platform. It exists across on-prem databases, cloud object stores, legacy warehouses, open data lakes, APIs, and external sources in multiple formats such as Parquet, Avro, and ORC. Different teams need different tools—AI teams may prefer Databricks, while BI teams lean on Snowflake, and operations rely on transactional databases.

So the issue isn't choosing between platforms. It's unifying and governing data across all systems without friction or compromise.

The Future: Federated IT and Portable Data Catalogs

Forward-thinking organizations are adopting a federated IT strategy—a model that embraces data and tool diversity while maintaining unified governance and seamless access. Central to this approach is the evolution of open and portable data catalogs.

Unlike traditional metadata repositories, modern data catalogs serve as the governance backbone. They allow for data discovery, consistent security policies, and interoperability with multiple query engines—freeing users to choose the best tools without forced migrations.

Key enablers include:

These catalogs provide consistent metadata, governance, and access across clouds, formats, and query engines.

Delivering Data for BI and AI with Flexibility

The battle between Snowflake and Databricks distracts from what truly matters: empowering teams to curate and deliver data efficiently. To achieve this, organizations should focus on:

  • A federated data architecture supporting diverse storage and processing systems
  • Interoperability across hybrid and multi-cloud setups
  • A portable catalog layer abstracting format and engine complexity
  • Self-service access for BI and AI teams, without bottlenecks

This approach reduces reliance on specific vendors and ensures adaptability to future workloads and tools.

The Role of Data Products in a Federated Platform

Federated IT isn't just about unifying access—it enables data products: curated, governed datasets with clear ownership. Like traditional product management, data products deliver consistent, high-quality resources tailored to specific needs, from BI dashboards to AI models.

Key benefits of data products in a federated model include:

  • Built-in governance and compliance
  • Format and engine flexibility
  • Self-service access with clear documentation and stewardship

The Unanswered Question: Scaling Federated Data Product Delivery

As organizations move toward federated models, several implementation challenges remain:

  • Standardizing definitions of data products
  • Implementing governance across clouds and on-prem environments
  • Automating pipelines for creation and maintenance
  • Balancing flexibility with control and compliance

Vendors are racing to address these hurdles, each promoting their vision for federated data delivery at scale.

Open Table Formats & Lakehouse Catalogs: Building Blocks of Federation

The foundation of this transformation lies in open formats like:

And in catalogs such as:

These technologies ensure data longevity, reduce vendor lock-in, and simplify governance while enabling fast, flexible access for analytics and AI.

From Platform Wars to Strategy: Embrace Federation

Instead of choosing a side in the Snowflake vs. Databricks debate, organizations should prioritize open architectures and federated governance. A platform-agnostic strategy supports BI, AI, and operational analytics without compromising flexibility or control.

By adopting open standards and portable catalogs, companies can build a future-proof data architecture that allows seamless access, secure governance, and scalable data product delivery—no matter what tools they choose.

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