Fivetran is a fully managed cloud data integration platform that automatically moves data from hundreds of business applications, databases, and event sources into a centralised data warehouse. It uses an ELT (extract, load, transform) approach, handles schema changes automatically, and removes most of the engineering work traditionally required to build and maintain data pipelines.
If you've heard the name in the context of the modern data stack but never quite pinned down what Fivetran actually does, this guide walks through the platform from first principles: what it is, how it works, what it's used for, and where it fits alongside tools like Snowflake, BigQuery, and dbt.
What Is Fivetran?
Fivetran is a fully managed data movement platform that automates the extraction, loading, and ongoing synchronisation of data from source systems into a cloud data warehouse or data lake. Founded in 2012 and headquartered in Oakland, California, it has become one of the most widely adopted tools in the modern data stack.
The platform's core promise is simple: instead of writing and maintaining custom code to pull data out of Salesforce, your production database, Google Ads, or any of hundreds of other sources, you click to configure a connector, point it at your destination, and Fivetran handles the rest.
That includes the initial historical load, ongoing incremental syncs, automatic schema migrations when source data changes, and recovery from failed syncs.
Fivetran serves more than 6,300 customers globally, with a connector library that has grown past 700 pre-built integrations.
How Fivetran Works: The ELT Approach in 3 Steps
Fivetran follows an ELT workflow rather than the older ETL pattern. The difference matters: ELT lets you load raw data into your warehouse first and transform it there, taking advantage of the elastic compute power of modern cloud warehouses. Here's how the three steps work in practice.
Step 1: Extract
A Fivetran connector authenticates against your source system (a SaaS app, database, file store, or event stream) and pulls data on a schedule you define. For databases, this typically uses change data capture (CDC) to read the transaction log and pick up only the rows that have been added, updated, or deleted since the last sync. For SaaS apps, it queries the source's API for changes.
Sync frequencies range from once a day to every few minutes, depending on your plan and the source system's capabilities.
Step 2: Load
The extracted data lands in your destination warehouse with zero transformation in transit. Tables, columns, and data types are replicated as faithfully as possible from the source. If your source adds a new column or changes a data type, Fivetran detects the change and propagates it to the destination automatically through a feature called schema migration.
This "load first, ask questions later" approach is the defining characteristic of ELT and the reason Fivetran can offer such low-maintenance pipelines.
Step 3: Transform
Once your raw data is in the warehouse, you model it into analytics-ready tables using SQL. Most Fivetran customers pair the platform with dbt (data build tool) for this step, either through dbt Cloud or Fivetran's native dbt Core integration. The transformation runs inside the warehouse, using its compute engine rather than a separate processing layer.
If you'd like to dig deeper into the value of automating your data pipelines, there's a good case to be made beyond just engineering convenience.
Fivetran vs Traditional ETL Tools
The difference between Fivetran's ELT model and traditional ETL is more than a reshuffling of letters. It reflects a fundamental shift in where transformation work happens and who can do it.
Traditional ETL:
- Extracts data from sources
- Transforms it in a separate processing layer (often a dedicated server or ETL tool)
- Loads the transformed result into the warehouse
- Requires upfront schema design and ongoing engineering maintenance
- Slower to adapt when source data structures change
Fivetran's ELT approach:
- Extracts data from sources
- Loads raw data directly into the warehouse
- Transforms inside the warehouse using SQL (typically via dbt)
- Schema changes propagate automatically
- Analysts and analytics engineers can own transformation logic without deep engineering support
The shift became practical because cloud warehouses like Snowflake, BigQuery, and Redshift made compute cheap enough that transforming data after loading is no longer a bottleneck. Fivetran's own resource on ETL vs ELT covers the architectural reasoning in more detail.
What Fivetran Is Used For
Fivetran is used by data teams to consolidate data from disparate systems into a single warehouse so it can be analysed, modelled, or activated. The most common use cases include:
Centralising SaaS application data
Teams use Fivetran to pull data from CRMs (Salesforce, HubSpot), marketing platforms (Google Ads, Meta Ads, HubSpot Marketing), support tools (Zendesk, Intercom), finance systems (NetSuite, QuickBooks), and product analytics tools into one place for unified reporting.
Database replication for analytics
Rather than running heavy analytical queries against production databases (and risking performance issues), teams replicate operational databases like PostgreSQL, MySQL, SQL Server, and MongoDB into a warehouse using Fivetran's CDC connectors.
Marketing analytics consolidation
A common starting use case: a marketing team wants campaign performance across paid channels, organic, email, and CRM in a single dashboard. Fivetran connects the sources, lands the data in Snowflake or BigQuery, and a BI tool like Tableau presents the unified view.
Powering AI and ML workloads
As organisations build AI applications and predictive models, they need reliable, current data pipelines feeding their training and inference workflows. Fivetran has positioned itself increasingly as infrastructure for AI use cases, not just traditional BI.
A real example: when Flibco wanted to centralise its operational data for analytics, the team used Fivetran to land data in Snowflake, dbt to transform it, and Tableau to visualise it. You can read the full Flibco case study for how the modern data stack came together end to end.
Fivetran's Connector Library and Destinations
A connector is a prebuilt data pipeline that Fivetran maintains for a specific source system. It handles authentication, API or log parsing, schema mapping, and incremental sync logic so you don't have to.
The platform currently offers more than 700 connectors across several categories:
- SaaS applications: Salesforce, HubSpot, Zendesk, Shopify, Stripe, Workday, NetSuite, and hundreds more
- Databases: PostgreSQL, MySQL, SQL Server, Oracle, MongoDB, DynamoDB
- Event streams: Segment, Snowplow, webhooks
- File stores: Amazon S3, Google Cloud Storage, Azure Blob, SFTP
- Advertising and marketing: Google Ads, Meta Ads, LinkedIn Ads, Google Analytics 4
- Custom and function connectors: for building integrations to niche or proprietary sources
On the destination side, Fivetran supports the major cloud data warehouses and lakehouses:
- Snowflake
- Google BigQuery
- Amazon Redshift
- Databricks
- Azure Synapse
- PostgreSQL (as a destination)
Most Fivetran deployments today land data in Snowflake or a similar cloud warehouse, since the platform is built around the assumption that your destination has near-unlimited compute available for downstream transformation.
How Fivetran Fits Into the Modern Data Stack
Fivetran rarely operates in isolation. It's almost always one component of a broader architecture that includes a destination warehouse, a transformation layer, and a presentation layer.
A typical modern data stack looks like this:
- Sources — SaaS apps, production databases, event streams, file stores
- Ingestion (Fivetran) — moves data from sources into the warehouse with minimal engineering overhead
- Warehouse (Snowflake, BigQuery, Redshift, Databricks) — stores raw and modelled data, provides compute for transformations
- Transformation (dbt) — turns raw landed data into clean, tested, documented analytics models
- Activation (Tableau, ThoughtSpot, reverse ETL tools) — surfaces data to business users through dashboards, search, or back into operational tools
Each layer does one thing well, and the boundaries between them are deliberate. Fivetran's role is the first half of the second step: getting the data in. Everything downstream depends on that ingestion layer being reliable, complete, and current. If you'd like a deeper walkthrough of how these pieces fit together, our guide to the modern data stack covers each layer and the principles behind the architecture.
Frequently Asked Questions
Is Fivetran an ETL or ELT tool?
Fivetran is primarily an ELT tool. It extracts data from sources and loads it into a destination warehouse in raw form, leaving the transformation step to be handled inside the warehouse, typically using SQL or dbt. The platform does offer some lightweight in-transit operations like hashing for sensitive columns, but the core philosophy is load first, transform later.
What does a Fivetran connector do?
A Fivetran connector is a prebuilt pipeline that handles everything needed to move data from one specific source system into your destination warehouse. That includes authenticating with the source, reading data through its API or transaction log, mapping fields to a normalised schema, performing the initial historical load, and running ongoing incremental syncs. Fivetran maintains over 700 connectors and updates them as source APIs change.
Who uses Fivetran?
Fivetran is used by data engineers, analytics engineers, and data teams at companies ranging from early-stage startups to large enterprises. Notable customers include Autodesk, Condé Nast, JetBlue, and Morgan Stanley. It's especially popular with mid-market and enterprise organisations that want to stand up a modern data stack quickly without building and maintaining custom pipelines.
Conclusion
Fivetran is, at its core, a way to remove the toil of data integration. Instead of writing custom code to pull data from each source system, your team configures connectors and lets the platform handle extraction, loading, schema management, and recovery.
It pairs naturally with cloud data warehouses, dbt for transformation, and a BI layer for presentation, forming the spine of what most teams now call the modern data stack.
Whether it's the right choice depends on your specific sources, latency requirements, and how much pipeline customisation you actually need, but for the majority of analytics workloads it has become a default for good reasons: reliability, breadth of coverage, and dramatic reduction in engineering overhead.
