dbt and Fivetran have announced their intent to merge. On October 13, 2025, the two companies announced an all-stock merger, positioned to bring data ingestion and transformation closer together.
That’s the headline. This is also giving another signal, that the modern data stack is converging. After years of tool sprawl, buyers are voting for more integrated, purpose-built stacks that reduce integration complexities, accelerate Artificial Intelligence (AI) delivery, and drive better a Return on Investment (RoI) through improved governance and cost efficiency. The dbt–Fivetran merger reflects this shift, bringing ingestion, transformation, and metrics closer together to create more unified, outcome-driven data stacks.
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Convergence isn’t about “one tool to rule them all”; it’s about fewer providers, built-in guardrails and consolidated data that travels across the stack. Both companies have indicated that their existing products will continue to operate independently for now, providing continuity for current users even as the broader market moves toward greater consolidation.
Zooming out, the dbt–Fivetran news is a symptom of a bigger shift; data teams are converging their stacks to get governed outcomes faster. Here’s why that convergence is happening now:
- High integration tax: Too many handoffs between tools cause delays, breakages, and rework
- Fragmented data foundation: Disconnected systems slow down analysis, create inconsistent metrics, and make it harder to operationalize AI and analytics at scale
- Rising regulation and risk management: Increasing demands around privacy, data quality, and platform resilience are pushing firms to centralize governance for compliant and reliable estates
- Escalating cloud spending: FinOps (cloud cost management) favors simpler architecture with less duplication, less egress, and less idle compute
How convergence is taking shape across the data stack
We’ve discussed the drivers, now let’s look at how this convergence is materializing in practice. The figure below illustrates where providers are bringing capabilities closer together, enabling data to move from source to decision with fewer handoffs and greater cohesion.
The dotted outlines mark zones where capabilities are being bundled for seamless processes:
- The governance layer (catalog, quality, observability, security/privacy)
These controls are coming together into one governed foundation with shared metadata, lineage, testing, and policy enforcement
Outcome: Fewer policy gaps, simpler audits, and higher reliability
- Core data engines (Relational Database Management System (RDBMS), NoSQL, Vector/Graph)
Generative AI (gen AI) requires multimodal data. Warehouses and operational stores are now expected to handle relational, documents, vector, and graph in one system or suite
Outcome: Fewer databases to manage and one foundation for Business Intelligence (BI), search, and AI features
- Embedded ingestion
Ingestion is now built directly into analytics, BI, and transformation tools, no longer a standalone step. Data flows through connectors, Change Data Capture (CDC), and event streams into shared environments with common rules and metrics, producing cleaner, analysis-ready outputs
Outcome: Faster time-to-signal, one set of definitions for batch and real-time, fewer custom pipelines
- Analytics + integration + storage (advanced analytics, data integration, warehouse, lake/lakehouse)
Analytics is converging with upstream integration and downstream activities connecting ingestion, modeling, and delivery within unified environments
Outcome: Shorter path from data collection to insights and actions, with lower orchestration and egress overhead
How providers are responding to convergence
We’ve seen where convergence is happening, now, here’s how providers are responding and positioning themselves to win.
- Salesforce + Informatica: Salesforce’s acquisition of Informatica expands its AI platform by bringing data integration, cataloging, privacy, and governance under one roof. The move strengthens Salesforce’s ability to deliver governed, end-to-end data flows that power agentic and AI-driven use cases
- ServiceNow + data.world: ServiceNow’s acquisition of data.world enhances its AI workflow platform by integrating advanced data cataloging and governance capabilities. The move strengthens ServiceNow’s ability to deliver context-rich, governed data that fuels intelligent automation and AI-driven operations
- Databricks + Neon: Databricks’ acquisition of Neon adds a cloud-based PostgreSQL database to its Lakehouse platform, enabling developers and AI agents to store, access, and manage data in real time. This deepens Databricks’ reach across both data management and AI enablement layers
Convergence is reshaping how enterprises think about data, value, and speed. The shift isn’t just technical, it’s strategic. Providers that align with this reality, bridging silos and building shared trust across the stack, will move from being data tool providers to becoming data partners.
If you found this blog insightful, you might also enjoy our recent viewpoint, Modern Data Stack (MDS) 2.0: The Future-ready Architype for Data-enabled Transformation – Everest Group Research Portal, which explores another topic relating to Data Stacks.
For more information on the ongoing convergence of the data stack, and what it means for providers, please reach out to Mansi Gupta ([email protected]) or Mehek Sethi ([email protected]).
