The Fivetran + dbt + Snowflake stack is generating buzz across modern data stack teams. Some are exploring it for the first time, curious about the hype, while others are already using one or two of these tools and want to know how they fit together.
This stack automates the flow of data sources to insights. Fivetran handles extraction and loading, dbt manages in-warehouse transformations with modular SQL, and Snowflake provides fast, scalable compute and storage.
In this guide, you’ll see what each tool brings to the table and how they work as a unified pipeline. Whether you’re evaluating the stack or optimising what you already use, this breakdown will help you make better decisions.
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Is Fivetran + dbt + Snowflake the Ultimate Modern Data Stack?
The Fivetran + dbt + Snowflake stack is a popular choice for teams that want to automate their data pipelines. Fivetran pulls data from different sources, dbt transforms it using SQL, and Snowflake stores it in a fast, scalable data warehouse. Together, they make it easier to manage data without writing a lot of custom code.
That said, it’s not always the right fit. It can get expensive as data grows, and it may not work well for real-time or advanced machine learning use cases. In those cases, tools like Hevo, Databricks, or open-source options might be a better fit.
Let’s break it down.
Fivetran: Move Your Data Without Lifting a Finger
At companies like Amazon, where marketing runs on Meta ads, logistics relies on custom databases, and finance uses NetSuite, aligning data across teams is a daily challenge. Fivetran addresses this by offering integrations that not only connect 700+ sources but also adapt to upstream changes automatically, saving engineering time that would otherwise go into maintaining brittle pipelines.
Fivetran’s strength lies in how it absorbs schema changes quietly — renaming columns, adjusting types, and handling historical syncs without breaking downstream models. This allows data teams to stay focused on modeling rather than patching pipelines every time a product manager names a field.
It’s a strong fit for organizations that prioritize stability and low-maintenance infrastructure. But if your team needs highly customized ingest logic, more control over orchestration, or tighter cost management, it’s worth evaluating Fivetran alternatives designed around flexibility rather than abstraction.
dbt: Turn Raw Data Into Clean, Usable Models
Most data teams start the same way, everyone writes their own SQL, and over time, no one knows which version is correct. dbt changes that by giving you a single place to define, test, and manage all your business logic inside the warehouse.
Instead of scattered queries, you get reusable models that build on top of each other, with version control, documentation, and testing baked in. It brings engineering discipline to analytics work, which makes things easier to scale and trust.
Teams that adopt dbt often see fewer bugs, faster onboarding, and cleaner handoffs between analytics and engineering. But if you’re working with non-SQL sources or need real-time processing, dbt’s warehouse-first, SQL-only approach might feel limiting.
Snowflake: Store and Analyze Data at Any Scale
Traditional data warehouses were rigid and slow to scale. Snowflake flipped that by separating storage and compute, so you can scale up for heavy workloads and scale down when things are quiet, all without touching your data or rewriting queries.
It’s not just fast, it’s also built for collaboration. Multiple teams can run workloads on the same data without stepping on each other, and features like zero-copy cloning make testing or sharing data feel effortless.
Snowflake works best for teams that want performance without the complexity of managing infrastructure. But if you’re running real-time systems or want full control over how compute is handled, you may want to weigh it against tools built for streaming or open-source stacks.
How These Tools Work Together
Fivetran, dbt, and Snowflake combine to meet modern ETL requirements with a streamlined ELT approach. Fivetran extracts data from diverse sources and loads it into Snowflake, handling schema changes automatically to reduce pipeline maintenance.
dbt then performs in-warehouse SQL transformations, turning raw data into clean, analytics-ready models. Snowflake provides scalable compute and storage, enabling fast, secure access to both raw and transformed datasets across teams.
Step-by-Step Flow: From Source to Insights
- Connect data sources in Fivetran: Choose from 700+ connectors like Facebook Ads, Salesforce, and NetSuite.
- Set up Snowflake as your destination: Fivetran automatically maps schemas and loads raw data into Snowflake’s staging area.
- Sync fresh data on schedule: Fivetran’s auto-scheduling keeps your Snowflake warehouse updated without manual triggers.
- Create dbt models for transformation: Write SQL-based models to clean, join, and document data. Use dbt’s features like ref() and tests.
- Run transformations and monitor results: Schedule dbt runs or trigger them after syncs. dbt Cloud handles CI/CD pipelines and documentation.
- Deliver insights through BI tools: Final models are ready for tools like Looker, Power BI, or Tableau connected to Snowflake.
This pipeline is ideal for teams looking to implement a fully automated ETL process in Snowflake using dbt and Fivetran.
Architecture Diagram and Setup Workflow
To make things easier, here’s a visual flow of how the Fivetran + dbt + Snowflake pipeline works:
The Role of Each Tool in the ELT Pipeline
Fivetran – Extract and Load
Fivetran handles the E (Extract) and L (Load) steps in the ELT process. It pulls data from your sources like Salesforce, Google Ads, or databases and loads it directly into Snowflake.
It standardizes messy, scattered data from dozens of tools and services into a clean, unified format, so by the time it hits Snowflake, it’s already organized, timestamped, and ready for downstream workflows.
Snowflake – Store and Process
Snowflake is the data warehouse where all your raw data is stored and processed. It is built to handle large amounts of data while staying fast and cost-effective.
Its architecture keeps storage and compute separate, so you can scale each one based on your needs. This makes it easy to manage performance without overspending.
dbt – Transform
dbt comes in after the data lands in Snowflake and takes care of the T (Transform) step. It uses SQL to clean and reshape raw data into models that are ready for analysis.
You can schedule these transformations, test the data logic, and check freshness automatically. This makes sure your reports are always built on clean and reliable data.
Key Benefits of Integrating the Three
- Get up and running fast. This ETL stack requires little to no engineering for daily operations.
- Scale without bottlenecks. Snowflake handles large data loads while Fivetran and dbt auto-adjust to schema changes.
- Spend less time fixing things. Schema drift and pipeline errors are automatically managed.
- Empower business teams with trusted data. dbt models include tests and documentation, and BI tools query cleaned data instantly.
- Build once, reuse often. This stack supports modular development and CI/CD, making it a favourite among mature data teams.
Use Cases & Case Studies
The Fivetran + dbt + Snowflake stack is a proven combination in real-world data teams across industries. Here’s how modern companies are applying it for specific, technical outcomes.
1. Smarter Customer Targeting
Fivetran pulls customer data from tools like Shopify, HubSpot, and Facebook Ads, then sends it into Snowflake automatically. This gives your team a single source to track what users do across all platforms.
With dbt, you can shape that raw data into clear customer groups, like repeat buyers or users who stopped engaging. These segments can then be used in ad targeting or CRM campaigns without extra effort.
Since dbt models are modular and reusable, your team doesn’t need to rewrite queries each time. Even if a data source changes, you can fix it in one place and stay consistent everywhere.
2. Product Usage and Analytics for SaaS
Fivetran moves event data, billing info, and user traits from tools like Stripe and Segment into Snowflake. This saves you from building and maintaining custom scripts for every data source.
dbt helps you organize that data into metrics like active users, top features, and churn. These models can be shared across teams, so everyone works with the same definitions.
Your dashboards become faster and more accurate, and your engineers spend less time fixing broken reports. It also makes experimentation easier since data is clean and always up to date.
3. Audit and Compliance Reporting
Fivetran also syncs log files, policy updates, and access records into Snowflake on a regular schedule. This creates a reliable, time-stamped history your team can use for audits.
dbt then structures that data so it’s easier to answer questions like who accessed what or when a change happened. You can also build alerts for anything unusual without needing external tools.
Since the logic is stored in dbt, it’s easy to track changes and explain them to auditors. This saves time and makes the entire audit process less stressful.
4. Executive Dashboards and OKRs
Fivetran pulls data from CRMs, analytics platforms, billing systems, and support tools into Snowflake, where it all stays up to date and in one place. This makes it easier for leadership to track business health without relying on scattered reports.
dbt helps define key metrics like revenue, active users, and LTV to CAC in a consistent, version-controlled way. The same logic is used across dashboards, so everyone sees the same numbers, whether they’re in marketing, finance, or product.
Once these models are built in dbt, they power dashboards that are always in sync with the latest data. Instead of chasing conflicting figures, leaders can make faster decisions based on reliable, agreed-upon metrics.
Common Challenges with the Fivetran + dbt + Snowflake Stack
This stack packs a serious punch when it comes to speed and flexibility. But like any setup, it’s not without its shortcomings.
1. Cost Surprises
Each tool has its own pricing model, and they don’t always play nicely together. Fivetran bills based on Monthly Active Rows, Snowflake charges for every second of compute, and dbt Cloud has tiered pricing that can jump quickly as teams scale or job concurrency increases.
When your data grows or syncs run more frequently, costs can spike quietly in the background. Many teams only realize the impact when the invoice lands. You’ll need active monitoring, warehouse auto-suspend settings, and maybe even cost caps to avoid overruns.
2. Schema Changes Break Everything
One renamed column in a source like Salesforce or Shopify can bring your pipeline to a halt. Fivetran may adjust for schema changes silently, but if your dbt models aren’t updated or tested against them, things still fail downstream.
Dashboards go blank, stakeholders raise questions, and you’re left debugging across multiple tools. Schema drift is common, and unless you’ve set up dbt tests and schema alerts, the breakage often hits without warning.
3. Steep Learning Curve
Each tool introduces a new layer of learning. Fivetran connectors require setup and ongoing management, dbt expects SQL and Jinja templating skills, and Snowflake adds its own SQL syntax, warehouse scaling options, and permission logic.
If your team has the expertise, that’s manageable. But for smaller companies or newer data hires, getting up to speed takes time. You’ll need thorough internal documentation and structured onboarding just to ensure things don’t break during handoffs.
4. Limited Real-Time and Streaming Support
This stack leans heavily on batch-based processing. Fivetran’s minimum sync interval is five minutes, and Snowflake isn’t optimized for streaming-style ingestion. If you’re working with IoT data, user events, or anything that needs real-time pipelines, this setup might not keep up.
While you can add tools like Kafka or Snowpipe, that adds more complexity and cost. For use cases that demand low-latency pipelines, the stack starts to show its limits.
5. Transformation Complexity and Skill Requirement
dbt is powerful, but only if your team knows how to wield it. Writing maintainable SQL models with tests, documentation, and sources takes practice. Refactoring transformations or scaling across teams adds even more overhead.
If your team lacks SQL fluency or prefers low-code approaches, managing transformations in dbt can feel like extra work rather than a time-saver.
6. Vendor Lock-In and Customization Constraints
Once your pipelines and transformations are deeply wired into Fivetran, dbt, and Snowflake, switching to other platforms isn’t easy. You build logic in dbt, depend on Fivetran’s connectors, and model data for Snowflake’s compute engine.
That limits flexibility down the road. Custom routing logic, complex data merges, or hybrid cloud deployments can be harder to execute when you’re locked into a specific workflow.
Hevo: A Smarter Fit for Simpler, Faster Pipelines
Fivetran works well for enterprise teams with complex ingestion needs and dedicated resources for managing tools like dbt. But if you want to move fast with fewer tools, Hevo replaces Fivetran’s ingestion layer and adds built-in transformation, monitoring, and automation in one place.
Hevo is better suited for teams that need quick setup, real-time data sync, and more flexibility in how transformations are handled. It supports both ETL and ELT workflows, making it easier to adapt pipelines without extra infrastructure or vendor lock-in.
Where Hevo Offers an Advantage:
- Built-in SQL and Python-based transformations
- Supports both ETL (pre-load) and ELT (post-load) workflows
- Auto Schema Mapping to handle changing source structures
- Real-time sync with Change Data Capture (CDC)
- Live pipeline monitoring and intelligent alerting
- Flat-rate pricing with transparent scaling
- No-code interface for easy setup and access across teams
Overall, Hevo simplifies your stack, boosts data reliability, and frees up your team to focus on insights instead of infrastructure.
Need a side-by-side feature comparison? Here’s everything you need to know about Hevo vs Fivetran.
Conclusion
Like any stack, this combination comes with trade-offs. Managing three separate tools can get complex, especially as your data needs grow. That’s where platforms like Hevo come in, offering a simpler way to build and run data pipelines with fewer moving parts.
If your team needs fast setup, lower maintenance, or more predictable pricing, Hevo might be the best fit. But if you have a strong data team and need deep customization, the Fivetran + dbt + Snowflake trio still holds up as one of the best in the business.
If you’re leaning toward a fully automated, no-code solution with 24×7 support and 150+ pre-built integrations (including 60+ free sources), Hevo is a great place to start. It takes the complexity out of data integration and helps you get up and running fast. Sign up for a 14-day free trial.
FAQs on Fivetran + dbt + snowflake stack
1. Fivetran, dbt, and Snowflake integration best practices?
Start by sticking to clear naming rules, syncing during off-peak hours, and testing your models often. Use dbt with Git to track changes, and set up Snowflake warehouses smartly so each job runs fast without stepping on another’s toes.
2. What are the key cost traps in this stack?
If you’re not careful, things like constant syncing in Fivetran or running heavy Snowflake queries can burn through the budget fast. Avoid full refreshes unless needed, and size your Snowflake warehouses right so you’re not overpaying for speed you don’t use.
3. How technical do I need to be to use this stack?
You don’t need to be a coding wizard. If you know some SQL and how data should look, you’re in good shape. Fivetran handles the plumbing, dbt helps clean things up, and Snowflake’s UI isn’t hard once you poke around.
4. What are the advantages of the Fivetran, dbt, and Snowflake stack?
You get a fast, reliable pipeline without piecing together tools from scratch. Fivetran pulls data in, dbt makes it clean and usable, and Snowflake handles scale like a champ. It’s flexible, modular, and grows with your team.
5. Can I use Fivetran without dbt?
Sure, you can load data with just Fivetran. But without dbt, cleaning and shaping that data turns messy real quick. If your team’s growing or needs clean reports, adding dbt makes your work a whole lot easier.
6. Why is dbt with Snowflake a winning combination for modern data teams?
Because they play to each other’s strengths. dbt’s transformation power plus Snowflake’s speed means you can model clean data fast, and test it too. It’s great when you want things to be accurate, scalable, and easy to maintain.
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