Data teams are rethinking how they build and manage analytics platforms. The modern data stack (MDS) brought about a data revolution—making analytics more accessible, fast, and affordable. Yet, this progress came with new challenges, particularly tool fragmentation and complex integrations. The industry now looks to open data infrastructure as a solution, aiming for flexibility and readiness for an AI-driven future.
Modern Data Stack: Achievements and Obstacles
The MDS was designed for agility, letting teams choose best-in-class tools for every stage, from ingestion to transformation. This sparked innovation and put practitioners in control. However, as organizations adopted more tools, they encountered:
- Integration headaches: More energy spent on connecting disparate tools than on delivering business value.
- Partial interoperability: Even with standards, resolving data quality, governance, and metadata issues proved difficult.
- Persisting governance challenges: End-to-end oversight remained fragmented and incomplete.
All-in-One Platforms: A Double-Edged Sword
To escape integration woes, many shifted to comprehensive solutions from cloud providers like Databricks, Snowflake, AWS, and others. These platforms promised simplicity by unifying tools, but introduced new downsides:
- Opaque, high costs: Bundled services and compute charges can drive up expenses unexpectedly.
- Limited flexibility: Vendor lock-in restricts adoption of new or best-fit technologies.
- Collaboration challenges: Siloed teams on different platforms struggle to work together efficiently.
Organizations face a stark choice: navigate the complexity of MDS or risk lock-in with all-in-one platforms. Neither delivers the full promise of agility and innovation.
Open Data Infrastructure: Bridging the Gap
Open data infrastructure offers a better path forward, defined by:
- Pluggability: Easily swap and connect components.
- Standards-driven integration: Rely on open, transparent standards, not proprietary APIs.
- Compute-agnostic design: Freedom to use any analytical engine or environment.
- Holistic governance: Unified management of data quality, governance, and metadata.
This approach lets organizations build powerful, flexible, and future-proof data systems—without the trade-offs of vendor lock-in or endless integration work.
The AI Imperative: Why Openness Matters
As AI adoption accelerates, high-quality, well-described data is vital. Open data infrastructure enables:
- Robust metadata: Central, standardized metadata to power both AI and analytics.
- Unrestricted data access: Use the best tools for evolving AI needs
- Seamless integration: Open standards such as MCP make it easy to adopt new AI tools
- AI-optimized compute: Support for engines built for AI, delivering speed and scalability.
Open data infrastructure does not mean starting from scratch. Instead, it provides the flexibility to assemble best-in-class, interoperable systems that are ready for whatever comes next.
dbt Labs and Fivetran: Advancing the Vision
The merger of dbt Labs and Fivetran is a major move toward making open data infrastructure a reality. Together, they aim to:
- Simplify the delivery of robust, customizable data platforms.
- Lower compute costs and increase efficiency.
- Address governance and data quality challenges holistically.
- Preserve customer choice and flexibility in tools and engines.
This partnership underscores that open data infrastructure requires collaboration and a shared commitment to openness—no single company can do it alone.
Building for the Future
Open data infrastructure marks a new era in analytics—combining the best of integration and freedom of choice. By embracing open standards and modular architectures, organizations can achieve greater agility, stronger governance, and continuous innovation—empowering them to thrive in the rapidly evolving world of AI.
Why Open Data Infrastructure Is the Future of Analytics and AI