Fivetran vs dbt: Everything You Need To Know About This Duo

Choosing between Fivetran and dbt can leave even experienced data teams second-guessing their stack. If you’ve browsed Reddit threads or Stack Overflow discussions, you’ve likely seen questions like “Do I need both?” or “Can dbt replace Fivetran?” These tools often appear side by side in modern analytics workflows, but many users are unsure of their actual roles. 

The confusion is understandable. Fivetran helps you automate data extraction and loading from hundreds of sources into your warehouse. dbt, on the other hand, transforms that raw data into clean, modeled outputs that are ready for analysis. They’re built for different stages of the ELT process, but without clear guidance, teams often use them interchangeably or over-engineer their pipelines. 

In this guide, we’ll unpack the core differences between Fivetran and dbt, explore where each tool fits best, and show you when using both makes sense. We’ll also introduce Hevo, a unified platform that simplifies the entire pipeline from source to insight. If you’re evaluating tools for a new data stack or trying to streamline what you already have, this comparison will help you make a confident, scalable choice. 

Fivetran vs dbt: Side-by-Side Summary

Here’s a quick look at Fivetran vs dbt to help you understand how they work in your data pipeline.

Overview of Fivetran

G2 Rating: 4.2(378)

Gartner Rating: 4.5(268)

What is Fivetran?

Fivetran is a fully managed ELT tool that automates the “Extract” and “Load” stages of your data pipeline. It connects to over 700 data sources, including Salesforce, HubSpot, and Google Analytics, and moves that data into cloud warehouses like Snowflake, BigQuery, Redshift, and Databricks.

Moving data into a central warehouse gives your team a single source of truth for analytics. Instead of running scattered reports across tools, you can query everything in one place using SQL. This speeds up analysis, improves reporting accuracy, and simplifies access for BI tools.

Fivetran requires no custom scripting, and it automatically adapts to schema changes. That means, if a column is added, removed, or renamed at the source, the data pipeline adjusts without breaking. Once set up, it runs in the background with minimal maintenance.

It’s ideal for teams that want reliable, low-effort data replication. With built-in monitoring, alerts, and support, Fivetran keeps pipelines stable and reduces time spent on troubleshooting — so your team can focus on modeling and insights, not data movement.

Key Features and Benefits

• 700+ Pre-built Connectors: Easily connect to tools across marketing, finance, support, and engineering like Salesforce, Google Ads, NetSuite, PostgreSQL, and Zendesk without writing any code.
  
• Change Data Capture (CDC): Keeps your warehouse up to date by syncing only the new or changed data, reducing both latency and storage costs.
  
• Automatic Schema Mapping and Evolution: Fivetran adjusts your pipeline when the structure of source data changes, so dashboards don’t break when a column is added or removed.
  
• Incremental Syncs: Transfers only the latest data during each sync, making the process faster and more efficient, especially for large datasets.
  
• Secure and Compliant Architecture: Meets top security standards like SOC 2 Type II, HIPAA, GDPR, and ISO 27001 to keep your data safe and audit-ready.
  
• Zero-Maintenance Pipelines: Built-in alerts, automatic retries, and re-syncs ensure your data keeps flowing with little to no manual work.
  
• Prebuilt Data Models (Fivetran Transformations): Get up and running quickly with ready-to-use transformation templates for popular data sources.

Pricing Model

Fivetran uses a consumption-based pricing model determined by Monthly Active Rows (MAR), offering a 14-day free trial for new users.

Overview of dbt

G2 Rating: 4.8(154)

Gartner Rating: 4.6(11)

What is dbt (Data Build Tool)?

Data build tool (dbt) is a transformation tool that helps you turn raw warehouse data into clean, reliable, and analytics-ready models. It’s built for modern data teams that need structure, testing, and documentation baked into their workflow. If you’ve ever struggled with scattered SQL scripts or hard-to-trace transformations, dbt brings everything into one place and makes it feel manageable.

The best part is that it runs almost entirely on SQL, so analysts don’t have to learn a new language to contribute. But it doesn’t stop there. dbt also supports Jinja, which lets you add dynamic logic to your SQL, and YAML files to manage settings like model dependencies or tests. This helps you avoid repeating the same code and makes your transformations easier to scale and maintain.

What makes the biggest difference is how dbt handles version control. Every change you make — whether it’s tweaking a model or renaming a column — gets tracked through Git. You can review it, test it, and even roll it back if something breaks. That’s huge when you’re working with multiple people or deploying to production. It brings the discipline of software development into analytics.

You can either go with dbt Core, which is open source and runs from the command line, or use dbt Cloud if you prefer a visual interface with built-in scheduling, logging, and team collaboration. Either way, it’s one of the most reliable ways to structure your transformation layer and scale it without losing visibility or control.

Key Features and USPs of dbt

• SQL + Jinja Templating: Lets you build dynamic, reusable models using familiar SQL along with Jinja for logic like loops and conditions.
  
• Model Dependencies and DAGs: Shows how models connect through a visual graph, making it easier to understand and manage data flow.
  
• Built-in Testing Framework: Helps catch data issues early, like duplicates, nulls, or broken joins, so bad data doesn’t reach your reports.
  
• Auto-Generated Documentation: Creates live, shareable docs for each model and column, helping teams understand what the data means.
  
• Git Integration: Tracks every change in your codebase, so you can review, collaborate, and safely roll back if something breaks.
  
• CI/CD Compatibility: Works with tools like GitHub Actions and GitLab to automate testing and deployment of models.
  
• Environment Support: Lets you separate development, staging, and production setups to safely test changes before going live.
  

dbt Packages: Offers reusable code modules (like macros) you can share or import, reducing repetitive work.

Pricing Model

dbt offers free community and paid enterprise editions with additional features and support. 

You can also take a look at the in-depth comparison of Hevo vs dbt and Coalesce vs dbt to understand which tool best fits your data transformation needs.

When to use Fivetran?

• You need to consolidate data from multiple tools: Fivetran offers hundreds of pre-built connectors, making it easy to centralize data from SaaS apps, databases, and more.
• You want fast, no-code setup: When speed matters, Fivetran’s no-code setup helps onboard new data sources in minutes without engineering help.
• Your data is siloed across departments: Disconnected tools create inconsistent reporting. With automated syncs into your warehouse, you can unify reporting across marketing, finance, and ops.
• You need reliable, low-maintenance pipelines: Automated monitoring, alerts, and retry logic reduce the need for manual intervention.
• You rely on near real-time data: Manual syncs are not built for speed. Change Data Capture keeps data fresh with minimal latency, ideal for time-sensitive use cases.

You’re working in a regulated industry

Compliance cannot be compromised. Fivetran meets compliance needs with certifications like HIPAA and SOC 2..

Therefore, choose Fivetran if your goal is fast, hands-free data ingestion that scales across tools, teams, and industries.

Advantages and Disadvantages of Fivetran

Advantages	Disadvanatges
Ease of use: User-friendly interface and automation reduce the requirement of technical expertise.	Limited Transformation Capabilities: It is primarily a tool for extraction and loading only. Complex transformations require it to be integrated with other tools like dbt.
Comprehensive Connectors: Over 500+ built in connectors, including major databases and SaaS applications.	Cost: This on-use model can be expensive for an organization with extensive data.

When to use dbt?

• You want structured, reusable data models : dbt helps you move from ad hoc queries to modular, scalable SQL transformations.
• You need collaborative workflows: Git-based version control ensures teams can track changes, test safely, and work in sync.
• You want built-in testing and validation: dbt makes it easy to write tests that catch issues early and protect key metrics. 
• You auto-updating documentation: dbt auto-generates model docs and lineage so teams can self-serve and understand the logic.
• You manage multiple environments: With macros and environment configs, dbt helps teams safely scale transformations across development, staging, and production.

So, if you’re looking for scalable, testable, and well-documented transformations that your analysts can own using just SQL, dbt is the tool to consider.

If you are looking to switch from Fivetran, check out our blog on Top 11 Fivetran Alternatives.

Advantages and Disadvantages of dbt

Advanatages	Disadvantages
Customizability: It is highly customizable through SQL and with dbt’s templating system.	Requires Technical Expertise: Requires good knowledge of SQL and data warehousing.
Open Source: Free to use, strong community, making it accessible for all sizes of organizations.	No Data Extraction: This is purely a transformation-focused component. Data extraction must be handled separately.

Fivetran and dbt Together

How can Fivetran and dbt complement each other?

• End-to-End Data Pipeline: Fivetran is responsible for extracting and loading data, and dbt handles transformation and modeling. Together, they combine to create a fully-rounded solution for building data pipelines.
• Efficiency and Automation: Fivetran automates Extract and Load processes, and dbt does the transformation and documentation.

Real-World Example and Case Study

Problem Number 1:

• Complex Data Management: SpotOn managed customer transaction data from over 2,000 lines of code and several MySQL databases, which created complex scaling and maintenance issues.
• Ineffective Reporting: Manual QA processes and the lack of version control also meant that the whole process took too long, sometimes 15 hours into the week, and reporting was slow.
• Scalability Issues: The reason behind this is that the engineering team had to write custom code for every new use case because it was not modular in data modeling, so this was a cumbersome process for scalability.
• Central Repository: It was hard for the system to generate the reporting for internal metrics because there was no central repository.

Solution:

• Fivetran and dbt integration: SpotOn integrated Fivetran with dbt (including Fivetran Transformations for dbt Core) to automate and streamline their ELT (Extract, Load, Transform) process.
• Automated Reporting: With Fivetran’s integration to dbt Core, models were run automatically following a load by the Fivetran connector, eliminating custom scripting or additional tools.
• Modular Data Models: dbt Core modularized existing complex transformation code, making it more readable, manageable, and scalable using Jinja templates.

Impact:

• Faster Reporting: With SpotOn, client-facing reporting became quicker, reducing the development time by a number of weeks.
• Efficient Data Management: Manage and automate a company’s entire ELT process from a single platform, improving its scalability and reliability.
• Internal Efficiency: The data engineering team empowered their analytics peers to support high volumes of data from multiple sources by leveraging dbt Cloud’s collaborative UI, scheduling, and alerting.
• Cost and Time Savings: It cut down development time by 5x and facilitated the reporting process internally and with clients with Spot On. All this was done without the loss of data integrity or scaling headcount.

Problem Number 2:

• Inefficient data processing: Red Ventures (RV) sought an efficient method to process client data; the existing process was consuming too much time and resources.
• High Engineering Workload: Setting up integrations and managing data transformations was highly resource-intensive by engineers, which constrained their attention to higher-value tasks.
• Troubleshooting Challenges: Integration and transformation problems require tremendous engineering time to troubleshoot.

Solution:

• Databricks, Fivetran, and dbt Integration: RV leveraged Databricks to scale data engineering pipelines, Fivetran for data ingestion, and dbt for data transformation. Each of these tools helped to support a machine learning pipeline, which could optimize client spending on advertisement.
• Data Democratization: The user-friendly aspects of Fivetran and dbt allowed for a broader set of employees to get involved in data tasks.

Impact:

• Cost Efficiency: Clients experienced up to 30 percent increased cost efficiency in marketing channels due to improved data utilization and AI-driven insights.
• Time Savings: Red Ventures saved 100 hours per typical data integration and reduced data processing time by 80 percent, enabling data engineers to focus on more strategic work.
• Reduced Troubleshooting: Engineering troubleshooting time was cut from 50 percent to less than 20 percent due to consistent data sets provided by Fivetran and clear transformation views offered by dbt.
• Enhanced Client Results: The democratization of data processes and improved efficiency led to better results for clients, helping them more effectively target customers.

Benefits of using both tools in a Data Stack

• Seamless integration—both tools are engineered to work harmoniously, ensuring fluid transitions from data extraction to transformation.
• Scalability: Both tools can grow with increasing data needs, making them well-suited for companies of any size.

Why do Hevo and dbt make a good pair?

Using Fivetran with dbt can give you a solid, modular setup. But managing two separate tools often means more complexity. You’re dealing with different interfaces, multiple billing cycles, and separate workflows for ingestion and transformation. Even when the integration works well, it adds overhead your team has to manage every day.

This is where Hevo Transformer changes the game. It combines data ingestion and SQL-based transformation within one platform, so there’s no need for tool handoffs or manual schema syncs. It also integrates with dbt, letting you run dbt models directly within Hevo for a more unified workflow. 

Hevo Transformer: Features and USPs

• Version control made easy 

Track, preview, and roll back transformation changes without relying on external tools or Git setups.

• Simplify dbt workflow automation 

Build and schedule transformations inside Hevo, or run dbt jobs natively through its integration. Either way, no extra orchestration is needed.

• Integrate data warehouse in minutes

Connect to 150+ sources and start syncing real-time data fast, with minimal setup or engineering support.

• All-in-one control features 

Monitor ingestion and transformation in one place. Set alerts, view logs, catch schema changes, and troubleshoot everything from a single dashboard.

Instead of juggling multiple tools, Hevo Transformer gives you ingestion, transformation, and monitoring in one platform. This makes onboarding faster, reduces handoffs, and simplifies maintenance.

It’s also more cost-effective. Fivetran charges about $1 per 1,000 active rows, and dbt Cloud starts at $100 per user per month. Hevo’s pricing starts at $239 per month for both, helping you save by avoiding separate tools and contracts.

Use Case Example: Teachmint Simplifies Its Stack

Teachmint, a leading edtech platform, switched to Hevo to simplify its fragmented data stack. The team had previously used multiple tools for ingestion and transformation, which created coordination overhead and slowed down reporting.

With Hevo, they consolidated both stages into a single platform. This reduced maintenance, improved visibility, and helped their analysts work faster with cleaner, more reliable data.

Final Verdict: Fivetran vs dbt vs Hevo

Choosing the right ETL tool isn’t just about picking the most feature-rich tools. It’s about how smoothly they fit into your team’s workflow. Whether you’re loading data from dozens of sources or building models for reporting, the setup should reduce friction, not add more steps.

Fivetran and dbt make a strong pair for teams that prefer modular tools. Fivetran handles ingestion reliably, while dbt gives you more control over transformation logic. But using both means switching between tools, managing configs separately, and keeping everything in sync.

Hevo offers a simpler alternative. It combines ingestion and transformation in one platform, with built-in orchestration, monitoring, and SQL-based modeling inside your warehouse. For teams that want a pocket-friendly ELT tool and spend less time on setup, Hevo brings everything under one roof.

If you are looking for other Fivetran Alternatives, have a look at some other comparisons

• Fivetran vs Supermetrics
• Fivetran vs Airflow
• Rivery vs Fivetran
• Fivetran vs Informatica

Conclusion

Although Fivetran and dbt are great solutions for data ingestion and transformation, challenges related to limited customizability and possible high costs are unmistakable. These thus create a challenge to flexibility and efficiency in Data Operations.

Learn about the key differences and use cases of Fivetran and ADF to help you optimize your data integration and transformation workflows.

Hevo addresses these concerns with integration with dbt, more customization options, and a friendly user interface. Hevo is cost-effective—with competitive, very open pricing—and offers an easy way to manage data that will grow with you as you scale. For more flexibility and cost-effectiveness, explore ways Hevo can complement your current data stack and optimize your data workflows.

FAQ Fivetran vs dbt