Fivetran and dbt Merge: What It Means for Data Teams

In 2026, the data science platform market alone is expected to hit $132.19 billion, reflecting how quickly teams are moving away from fragmented toolchains toward integrated solutions.

The Fivetran and dbt Labs merger in 2025 was one of the clearest signals of this shift. What started as a strategic move has now reshaped how teams build and operate pipelines, with tighter integrations, bundled pricing, and more centralized workflows.

A year later, the question is no longer what the merger could mean. It is what has already changed, and what data teams need to do differently to stay flexible in a platform-first world.

In this guide, we’ll discuss how the Fivetran and dbt merger reshapes data architecture, vendor strategy, and day-to-day pipeline operations.

Fivetran and dbt Merge Details

After Tristan Handy and George Fraser completed the merger of Fivetran and dbt Labs in October 2025, Tristan now serves as President of the combined company, while George continues to lead it as CEO.

The combined company operates at roughly $600 million in annual recurring revenue, serves over 10,000 customers, and supports a large global community of analytics engineers built around dbt. George shares,

“This is a refounding moment for Fivetran and the broader data ecosystem. As AI reshapes every industry, organizations need a foundation they can trust one that is open, interoperable, and built to scale with their ambitions. Our admiration for dbt and its remarkable community runs deep this is about bringing together the best of both worlds to accelerate innovation and create lasting impact across the data community.”

– George Fraser, CEO of Fivetran.

For those new to these tools: Fivetran is the go-to platform for automated data integration. It’s powerful but also known for premium pricing that can make your finance team gulp.

dbt Labs revolutionized how we transform data. Their open-source tool, dbt Core, turned SQL into a software engineering practice with version control, testing, and documentation. Their managed service, dbt Cloud, added scheduling and collaboration features that teams love.

The companies say they want to create an “open data infrastructure” that covers everything from data ingestion to transformation and activation, all while staying flexible about which warehouse or compute engine you use.

What Changes for Users

Since the merger, the combined entity has moved quickly to collapse the operational seams between the two tools. Here’s what it offers:

• dbt cloud orchestration inside the Fivetran UI: dbt Cloud jobs now trigger automatically once a Fivetran sync completes. This is managed entirely from the Transformations tab in Fivetran, removing the need for external orchestration tools or manual scheduling.

• Centralized pipeline monitoring: Connector alerts and dbt transformation run logs are now consolidated in a single Fivetran dashboard, ending the context-switching between two separate monitoring interfaces.

• No-code transformations via quickstart models: Fivetran’s quickstart data models deliver analytics-ready tables without any SQL, built on dbt Core under the hood and covering major connectors including Salesforce, HubSpot, and Shopify.

• Open source continuity: dbt Core and the Fusion engine will continue under their current licenses, with Tristan Handy leading open-source strategy. Fivetran’s existing portfolio of over 100 community dbt packages remains available.

Unified Billing (Pending): Once the transaction closes, Fivetran and dbt are expected to be consolidated into a single contract, subject to regulatory approval.

Why This Merger Makes Sense

1. Pressure from Big Players

AWS, Google Cloud, and Azure have embedded ingestion and transformation capabilities natively into their ecosystems, giving enterprise teams fewer reasons to add third-party vendors to their contracts.

For Fivetran and dbt, this created direct pressure on retention. Large accounts already committed to a single cloud provider could approximate both tools natively, often at no additional cost, making renewal conversations increasingly difficult to win.

Staying independent meant shrinking relevance. Merging was the cleaner path to competing above the hyperscaler layer rather than against it.

2. Competition from Unified Data Platforms

The competitive landscape has shifted toward unified data platforms. Open-source tools like Airbyte and dltHub still compete on cost, but the bigger pressure comes from Snowflake and Databricks, which now bundle ingestion, transformation, and orchestration into their ecosystems.

Standalone tools are harder to justify when the warehouse includes these capabilities.

Fivetran’s response has been aggressive. It acquired Census for reverse ETL, Tobiko Data for SQLMesh, and merged with dbt Labs in 2025. The goal is clear: shift from a connector to an end-to-end, compute-agnostic data infrastructure provider that can compete directly with native platform offerings.

The structural pressure remains because most revenue still sits at the compute and storage layer dominated by Snowflake and Databricks. Fivetran and dbt are expanding across data movement and modeling to stay central to analytics and AI workloads without locking teams into a single cloud ecosystem.

Evaluating your data stack options? Download Hevo’s ETL Tool Buyer’s Guide

What Data Engineers Are Saying?

The data engineering community has opinions about this merger. And they’re not holding back.

1. Concerns About Open Source

The biggest worry? What happens to dbt Core? The beloved open-source tool that launched a thousand data transformation careers.

Many fear it’ll become abandonware. New features will only land in the paid dbt Cloud version, while Core slowly withers. 

As one Reddit user put it: “Core is gonna stay unchanged while Cloud keeps gaining new features. Eventually, it will be end-of-life.”

But there’s a counter-argument too. dbt Core has over 2,000 forks on GitHub and a license that protects it from being yanked away. If Fivetran tries anything shady, the community can, and likely will, fork the project and keep it alive independently.

2. Pricing Worries

Fivetran recently raised prices 4-8x for some customers. Now they’re acquiring dbt. You don’t need a crystal ball to see where this might go. As one user bluntly stated: “dbt pricing going to the moon.”

History isn’t encouraging either. Remember when Salesforce bought Tableau? Or Google acquired Looker? 

Both saw stagnant innovation and climbing prices. The community is worried this merger follows the same playbook.

3. Questions About Strategy

Fivetran already owns SQLMesh, which is actually a dbt competitor with some more advanced features. So why buy dbt too?

They bought SQLMesh for the technology and dbt for the customer base. 80-90% of Fivetran’s customers already use dbt. There’s very little room for cross-selling. The overlap is almost complete.

Still, some optimism exists. Better integration between ingestion and transformation is valuable. Unified support might reduce friction. And if the merger pushes more Fivetran components toward open source? That could actually be good for everyone.

What is the Impact on the Modern Data Stack?

The Consolidation Trend

This merger isn’t happening in isolation. The entire data industry is consolidating. Microsoft has Fabric. Databricks has Lakeflow. Everyone’s building bigger, more integrated platforms.

The modern data stack’s original promise was “best-of-breed” tools that you could mix and match. Pick the best connector platform, the best transformation tool, the best orchestrator, and the best BI layer. It was flexible! It was powerful! It was also… kind of a mess.

Tool sprawl became a real problem. So did integration headaches. The consolidation we’re seeing now is the market’s correction. Companies trading some flexibility for simplicity and integration.

Orchestration Gaps

Here’s an interesting question: what’s missing from the Fivetran-dbt combo?

Orchestration, for one. Neither tool handles complex workflow orchestration particularly well. 

dbt Cloud has basic scheduling – run this model after that one finishes. Fivetran does simple sync schedules. 

But if you need complex dependencies, conditional logic, or cross-system orchestration? You’re still reaching for Airflow or Dagster.

Some in the community are speculating about future acquisitions. Will they buy Dagster next? Partner with managed Airflow providers? Or build their own orchestration layer? Time will tell.

What does this mean for your Data Team?

If You Use These Tools

Short term: Take a breath. Expect minimal changes right away. Your existing contracts will continue as-is.

Medium term: Watch for what happens at renewal time. Will they pressure you to consolidate contracts? Change pricing models? Bundle features differently? Pay attention to the signals in those conversations.

Long term: Start thinking about vendor lock-in. You don’t need to panic or rip everything out. But having an exit strategy, even if you never use it, gives you negotiating leverage and peace of mind.

If You’re Choosing New Tools

Consider pausing if you’re about to sign a long-term contract with either company. Let the dust settle and see how the merger actually plays out.

Evaluate alternatives now rather than waiting until you’re frustrated and desperate. Open-source options and independent vendors give you more flexibility and often better pricing.

Ask tough questions about pricing roadmaps, and open-source commitments. Don’t accept vague promises. Get specifics in writing.

Why Flexible, Vendor-Agnostic Tools Like Hevo Matter More Than Ever

The Fivetran–dbt merger has already changed how contracts are negotiated. Teams heading into 2026–2027 renewals are now dealing with a single vendor that controls both ingestion and transformation, often through bundled pricing. For teams that built modular stacks, this reduces leverage and limits flexibility at renewal.

Hevo Data offers a more predictable alternative. Instead of bundled contracts, it uses transparent, event-based pricing that gives teams clear visibility into what they are paying for at every stage of the pipeline.

For teams evaluating their options, Hevo’s key advantages are:

• No ecosystem lock-in: Works with Snowflake, Databricks, BigQuery, and Redshift without tying ingestion to any single vendor’s transformation or activation layer.

• Orchestration-friendly: Integrates with Airflow and supports conditional logic, fitting into existing workflows without a rip-and-replace.

• Transparent pricing: Fixed, predictable costs with no MAR-based billing surprises. For instance, Collectors, an authentication and grading service provider, saved 50% compared to Fivetran’s pricing after moving to Hevo.

• Migration support: Built-in tooling to migrate from legacy pipelines without extended downtime or re-engineering.

• Flexible integrations: Works across warehouses like Snowflake, Databricks, BigQuery, and Redshift without coupling ingestion to a specific stack.

• Proven stability: No open questions about roadmap consolidation, licensing shifts, or cultural integration that come with any major merger.

From raw source to analytics-ready in minutes; try Hevo free for 14 days.

The Current State of the Integrated Stack

Fivetran + dbt combined can take a technical direction set around open standards, compute-agnostic infrastructure, and AI-ready data pipelines.

What is already shipping:

• dbt Fusion with modern table format: dbt Labs introduced the Fusion engine with support for Apache Iceberg on platforms like Snowflake and Databricks. This helps teams align transformations with open table formats and improves cross-platform portability.

• dbt Agents and dbt MCP Server: dbt Agents and the remote dbt MCP Server let AI tools interact with dbt projects through a secure, governed interface. Teams can run dbt commands, explain logic, and assist development from tools like VS Code or dbt Studio.

Open Semantic Interchange and MetricFlow: dbt Labs has open-sourced MetricFlow, which powers the dbt Semantic Layer, under the Apache 2.0 license. This aligns with the Open Semantic Interchange standard, supported by partners like Snowflake and Salesforce.

What to watch heading into 2027:

The orchestration gap still exists. dbt Cloud and Fivetran do not natively handle complex workflows, so teams still rely on tools like Apache Airflow, Dagster, or Prefect.

How this gap is addressed, through acquisition or partnerships, is a key roadmap signal.

At the same time, 2026–2027 renewals will reveal how the combined entity prices its bundled stack, making contract cycles the clearest indicator of its long-term monetization strategy.

Building a Flexible Stack with Hevo

The Fivetran–dbt merger signals a shift toward bundled, end-to-end data platforms, simplifying workflows but increasing coupling across ingestion, transformation, and pricing. For data teams, the focus now is on staying modular and flexible.

Hevo Data gives teams more control with a warehouse-agnostic approach, allowing them to switch, scale, or customize their stack without vendor lock-in. Its fault-tolerant architecture ensures reliable data movement with automatic retries, and end-to-end observability offers clear visibility into pipeline health.

On the security front, Hevo meets enterprise standards with SOC 2 Type II compliance and GDPR readiness. Combined with transparent, event-based pricing, it simplifies budgeting by aligning costs directly with actual data usage.
Book a 1:1 consultation call with Hevo today and get your data pipelines up and running in minutes.