Fivetran vs dbt: Comparing Data Integration and Transformation Tools

We all know that data provides a competitive advantage, but raw data alone doesn’t do much good. Value is generated when your organization can reliably move, model, and transform its data to support real-time analytics, AI initiatives, and smarter decision-making.

This is where tools like Fivetran and dbt (Data Build Tool) enter the picture.

Fivetran is widely known for its powerful, automated data integration capabilities. It simplifies the process of extracting and loading data from hundreds of sources into a central repository. Alternatively, dbt excels when it comes to data transformation within the warehouse. It empowers analysts and engineers to build reliable, scalable models with version-controlled SQL code.

So which tool is right for your team? Should you use one, both, or consider an alternative entirely?

In this article, we’ll go over the differences, advantages, and use cases of Fivetran and dbt. We’ll also look at how they can complement each other and work together in modern ELT workflows then explore what to keep in mind as you decide what is the best fit for your stack.

What is Fivetran?

Fivetran is a fully managed, cloud-native platform designed to automate data extraction and loading. It simplifies the “E” (extract) and “L” (load) in ELT by offering pre-built connectors, real-time data synchronization, and a user-friendly interface that requires minimal configuration.

Instead of building and maintaining custom pipelines, data teams can use Fivetran to connect hundreds of data sources—like Salesforce, NetSuite, Google Analytics, and MySQL—and automatically load that data into a destination such as Snowflake, BigQuery, or Redshift.

Key features

• 500+ pre-built connectors: Seamlessly integrates with a broad range of databases, SaaS tools, and file storage systems.
• Real-time syncing: Keeps data continuously up to date, supporting timely analytics and operations.
• Schema drift handling: Automatically adapts to schema changes at the source.
• Consumption-based pricing: You only pay for what you use, measured by Monthly Active Rows (MAR).

Fivetran’s strength lies in how much it abstracts away. No need to write custom code or monitor jobs constantly—just plug in a connector, define your destination, and let it run.

What is dbt?

dbt (Data Build Tool) is an open-source transformation framework that enables data analysts and engineers to write modular SQL models that transform raw data into trusted, analysis-ready data sets. It operates entirely within the data warehouse, making it a critical component of ELT workflows.

Unlike traditional ETL tools that perform transformations before loading data, dbt works on data that’s already been loaded. It emphasizes transformation as a collaborative, maintainable, and testable step in the pipeline.

Key features

• SQL-first transformations: Uses simple SQL SELECT statements to define data models.
• Modular and reusable code: Break complex transformations into logical, testable blocks.
• Version control integration: Git-based workflows for collaborative development.
• Data testing and documentation: Built-in assertions and documentation generators.

With dbt, data teams can standardize how transformations are written and managed, enforce testing practices, and document their data assets—all using the tools analysts are already familiar with.

Side-by-side comparison of Fivetran and dbt

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When to use Fivetran

Fivetran is a great fit when:

• You need rapid integration with multiple data sources.
• Your team wants to reduce engineering workload and avoid building manual pipelines.
• You require real-time or near-real-time updates for operational reporting.
• You’re looking for a low-maintenance, set-it-and-forget-it data ingestion solution.
• Your architecture relies on modern cloud data warehouses (e.g., Snowflake, BigQuery, Redshift).

Use case 1:
A retail business wants to centralize marketing, ecommerce, and POS data to improve customer segmentation. With Fivetran, they can connect Shopify, Facebook Ads, and their transactional database in minutes, feeding that data into their warehouse for downstream analytics.

Use case 2:
A SaaS company needs to monitor customer behavior across its app, CRM, and support channels to proactively manage churn. Fivetran helps them pull data from Segment, HubSpot, and Zendesk into BigQuery, where data teams can build churn prediction models using real-time signals.

Use case 3:
A financial services firm is preparing for a regulatory audit and must consolidate data from various systems, including legacy databases, Salesforce, and internal compliance tools. Fivetran’s automated schema management and change data capture features simplify integration and ensure data is synced, reliable, and audit-ready with minimal manual oversight.

When to use dbt

dbt is ideal when:

• Your team is focused on clean, reusable data models for BI and analytics.
• You need flexible SQL transformations that analysts and engineers can both manage.
• You want to apply version control, CI/CD, and documentation to your data workflows.
• You're building production-grade analytics layers on top of your warehouse.
• You require data testing and governance in your transformation process.

Use case 1:
A finance team needs to transform raw transactional data into monthly financial statements and forecasts. With dbt, they can build modular models that calculate revenue, cost, and margin—complete with documentation and automated tests for each model.

Use case 2:
A healthcare analytics company needs to normalize patient data coming from different hospital systems to support consistent reporting and regulatory compliance. Using dbt, they create standardized models and apply automated tests to ensure clean, reliable outputs, which is critical for both care outcomes and HIPAA reporting.

Use case 3:
A media company is tracking engagement across multiple digital platforms. With dbt, the data team creates centralized models to unify web traffic, ad impressions, and video views, enabling the marketing team to build accurate attribution models and optimize campaign strategies.

Why many teams use both

Fivetran and dbt are often used together as part of a modern ELT stack:

• Fivetran extracts and loads raw data from dozens of sources into a central cloud data warehouse.
• dbt transforms that raw data into meaningful, analysis-ready models that power dashboards and machine learning workflows.

This separation of concerns is one of the biggest strengths of the ELT approach. By decoupling ingestion from transformation, teams can scale more easily and maintain cleaner pipelines.

Use case 1: B2B SaaS company improving ARR forecasting
A SaaS company uses Fivetran to ingest data from Salesforce, Stripe, and product usage logs into Snowflake. With dbt, their analytics team builds revenue recognition and customer lifecycle models that power dashboards for churn, ARR, and upsell forecasts. This aligns finance, sales, and customer success on shared metrics.

Use case 2: CPG brand enabling omnichannel performance reporting
A consumer goods brand integrates Shopify, Amazon Seller Central, Meta Ads, and retail POS data using Fivetran. Their data team then uses dbt to unify product IDs, map channels, and calculate contribution margins by SKU and channel—fueling real-time dashboards in Looker for the marketing and operations teams.

Use case 3: Healthcare startup building a single source of truth
A digital health company pulls patient onboarding, appointment, and engagement data from multiple tools using Fivetran. dbt is used to apply transformations that de-identify sensitive data, standardize clinical metrics, and document the data lineage. This ensures analytics, compliance, and care teams all work from a single trusted data set.

The limitations of Fivetran and dbt

No tool is perfect. Here’s where each one may fall short:

Fivetran limitations

• Limited transformation logic: While Fivetran offers basic data transformation via connectors, it’s not built for complex modeling.
• Cost at scale: MAR-based pricing can get expensive with large data volumes or high-frequency syncs.
• Customization constraints: Less flexibility for unusual schemas or complex use cases.

dbt limitations

• No ingestion capability: dbt only transforms data already present in the warehouse. It doesn’t handle extraction or loading.
• Requires SQL proficiency: Non-technical teams may struggle without sufficient training.
• Batch-oriented: Not designed for real-time transformations.

What about alternatives?

Depending on your organization’s priorities, there may be other tools worth evaluating.

Hevo

Hevo offers no-code data integration and transformation, combining many of Fivetran and dbt’s benefits in a single platform. It’s especially popular with business users who want to reduce the number of tools in their stack.

Key differentiators:

• Real-time ingestion
• Built-in transformation (Hevo Transformer powered by dbt Core)
• Transparent, usage-based pricing

Ideal for: Teams seeking a balance between automation and customization without the overhead of managing multiple tools.

Coalesce

Coalesce is another emerging transformation platform that combines visual modeling with dbt-style transformation logic. It's built for enterprise data teams who want governance, performance, and scalability with a GUI-based experience.

Choosing the right tool for your stack

Here’s how to evaluate which tool(s) best fit your data strategy:

1. What’s your biggest challenge: ingestion or transformation?

• Choose Fivetran if you need fast, reliable ingestion.
• Choose dbt if you already have loaded data but need clean, reusable models.

2. Who’s using the tool: engineers, analysts, or business users?

• Fivetran is user-friendly and can be managed by operations or BI teams.
• dbt requires more technical fluency but offers deep transformation power.

3. What’s your tech stack?

• If you’re already using a cloud data warehouse, both tools will likely integrate well.
• Consider dbt Cloud or Fivetran's integrations to minimize deployment time.

4. What are your compliance and governance needs?

• dbt offers granular testing and documentation capabilities.
• Fivetran supports enterprise-grade security and certifications.

5. What’s your budget tolerance?

• Fivetran’s cost can add up based on usage volume.
• dbt is open-source, but the enterprise edition adds functionality and support.

The future of data pipelines

As the modern data stack continues to evolve, we’re seeing more emphasis on:

• Composable architectures
  Organizations are moving away from monolithic platforms in favor of modular, best-in-class tools that handle specific stages of the data lifecycle. This approach enables flexibility and faster innovation, allowing teams to mix and match tools like Fivetran, dbt, Snowflake, and Looker based on their unique needs.
• Data observability
  With data driving mission-critical decisions, visibility into pipeline performance, lineage, and quality is now essential. Tools that offer built-in monitoring, alerting, and root cause analysis are becoming foundational—not optional.
• AI integration
  Businesses are embedding AI and machine learning directly into their data workflows, from real-time anomaly detection to predictive analytics. The future of data pipelines includes native support for model training, deployment, and monitoring.
• Self-service access
  As data literacy grows, more organizations are empowering non-technical users to access and explore data securely. Platforms that offer intuitive interfaces and role-based access controls are helping close the gap between data producers and consumers.

Fivetran and dbt are shaping this future by making it easier for organizations to automate, scale, and govern their data operations with confidence. Together, they lay the groundwork for agile, intelligent, and resilient data ecosystems that support innovation at every level.

FAQ: Fivetran vs dbt

Can I use Fivetran without dbt?
Yes. Fivetran can be used independently for automating data ingestion into your warehouse. However, for complex modeling and analytics-ready data sets, you'll typically need a transformation layer (either dbt or another tool).

Can I use dbt without Fivetran?
Yes. dbt works with data already in your warehouse, regardless of how it got there. You can pair dbt with other ingestion tools like Airbyte, Stitch, or custom pipelines.

Is dbt a replacement for Fivetran?
No. dbt focuses exclusively on the “Transform” step in ELT. It does not extract or load data. Instead, it complements tools like Fivetran by transforming raw, loaded data into usable insights.

Do I need to know SQL to use dbt?
Yes. dbt is built around SQL, so familiarity with SQL is required. However, dbt’s modular structure, templating, and community documentation make it accessible even for analysts without deep engineering backgrounds.

What’s the cost difference between Fivetran and dbt?
Fivetran charges based on data volume (Monthly Active Rows), which can become costly at scale. dbt is free as an open-source CLI tool but offers premium features through dbt Cloud, including a managed UI, scheduling, and support.

Which tool is better for real-time data?
Fivetran supports real-time or near-real-time syncs. dbt runs on scheduled batch jobs, making it less suited for real-time use cases without additional orchestration.

What if I want to reduce tool sprawl?
Consider platforms like Hevo, which combine ingestion and transformation in a single tool, or explore dbt Cloud’s integrations with ingestion tools for simplified management.

Final thoughts: What’s right for you?

Ultimately, the best choice for you comes down to your data maturity, team structure, and business goals. Here are some options to consider:

• If your priority is data ingestion, go with Fivetran.
• If your focus is transformation and modeling, use dbt.
• If you want end-to-end automation, use both together.
• If cost, simplicity, or flexibility are constraints, explore tools like Hevo or Coalesce.

This is an opportunity for you to start small with a pilot project, monitor how well the tool scales with your data growth, and choose platforms that align with your long-term analytics vision.

Ready to go beyond integration and transformation?

Domo brings it all together—data ingestion, transformation, real-time dashboards, and AI-powered insights—in one unified platform. Whether you're using Fivetran, dbt, or both, Domo helps you turn raw data into business decisions faster.

Explore how Domo can simplify your modern data stack. Watch a demo now.