Fivetran

Definition

Fivetran is a company that provides an automated data integration platform using the ELT (extract, load, transform) pattern to move data from source systems such as SaaS applications, databases, and event streams into a central data warehouse. It maintains prebuilt connectors that automatically adapt to source schema changes, reducing the custom pipeline code teams need to write and maintain. Fivetran addresses the recurring engineering cost of writing and babysitting bespoke extraction scripts against dozens of external APIs, each with its own authentication, pagination, and rate-limit quirks, by centralizing that maintenance inside a shared, tested connector catalog.

Overview

Fivetran's core product is a managed set of data connectors that extract data from sources like Salesforce, Google Ads, PostgreSQL, and dozens of other systems, then load it into a destination warehouse such as Snowflake, BigQuery, Redshift, or Databricks. Unlike traditional ETL tools that transform data before loading it, Fivetran follows the ELT pattern: raw data is loaded first, and transformation happens afterward inside the warehouse, often using a tool like dbt. This shifts the transformation workload onto the warehouse's own compute rather than a separate ETL engine, which is the structural choice that most differentiates ELT tools from older ETL platforms. A central mechanical detail is automated schema handling: when a source system adds a column or changes a data type, Fivetran's connectors detect the change during a scheduled sync and propagate it to the destination schema automatically, which reduces the maintenance burden compared to hand-written extraction scripts that break silently when an upstream API changes shape. Connectors are configured through a web interface with minimal custom code, and sync schedules can run as frequently as every few minutes depending on the connector and pricing tier, with change data capture used for database sources to avoid re-scanning entire tables. Fivetran occupies the extraction-and-loading layer of the modern data stack, sitting upstream of transformation tools like dbt and downstream of the source systems themselves; it does not perform complex in-flight transformation the way older ETL platforms like Informatica or Talend do, leaving that work to the warehouse. In practice, teams use it to centralize SaaS application data, replicate operational database tables for analytics, and feed raw tables into dbt-based transformation pipelines without writing custom extraction code for each source. Fivetran prices primarily by monthly active rows, a metric based on the volume of unique rows synced and updated, rather than by connector count or compute time. This model is straightforward for predictable workloads but can become expensive for very high-volume or highly mutable datasets, since costs scale with data change volume rather than a flat subscription. Fivetran positions itself against both open-source data integration tools, such as Airbyte and Meltano, and other managed integration vendors. Its advantage is a wide catalog of maintained, tested connectors and low operational overhead; a limitation is less flexibility for highly custom extraction logic compared to open-source frameworks that expose lower-level access to pipeline internals, which is where teams with unusual source systems or strict cost constraints often look instead.

Key Features

• Prebuilt connectors for hundreds of SaaS apps and databases
• Automated schema drift detection and propagation to the destination
• ELT architecture loading raw data before transformation
• Configurable sync frequency down to near-real-time for some connectors
• Pricing based on monthly active rows rather than compute time
• Integrates with dbt for downstream transformation workflows
• Supports major cloud warehouses including Snowflake and BigQuery
• Change data capture support for database sources

Use Cases

Alternatives

Frequently Asked Questions

Related in AI Tools