Overview

Fivetran is a cloud-native data integration service that automates the Extract, Load, Transform (ELT) process, designed to centralize data from various operational systems into cloud data warehouses for analytics and business intelligence. Founded in 2012, the platform focuses on providing pre-built, managed connectors that handle schema changes, data normalization, and incremental updates automatically, aiming to reduce the engineering effort typically required for data pipeline maintenance. This approach allows organizations to focus on data analysis rather than the complexities of data ingestion and transformation.

The core value proposition of Fivetran lies in its ability to connect to a broad spectrum of data sources, including databases, applications, events, and files, and then reliably load that data into destinations like Snowflake, Google BigQuery, Amazon Redshift, and Databricks. The service manages the entire lifecycle of data replication, from initial historical syncs to ongoing real-time updates, ensuring data consistency and freshness. For data transformation, Fivetran integrates with tools like dbt (data build tool), allowing users to define and execute transformations directly within their data warehouse after the data has been loaded. This post-load transformation aligns with the ELT paradigm, which prioritizes loading raw data first for flexibility in downstream analysis.

Fivetran serves a wide range of users, from small businesses to large enterprises, particularly those adopting a modern data stack centered around cloud data warehouses. It is often chosen by data engineers and analysts who require reliable, low-maintenance data pipelines to feed business intelligence dashboards, machine learning models, and operational analytics. The platform's emphasis on automation and pre-built connectors is intended to accelerate time-to-insight by abstracting away much of the complexity associated with data integration. While it offers a REST API for programmatic control of connectors, its primary mode of operation is through a web-based interface that supports low-code/no-code configuration for common data ingestion tasks. The platform provides comprehensive documentation for its connectors and API, guiding users through setup and management tasks Fivetran documentation portal.

Key features

  • Automated Data Connectors: Offers a library of hundreds of pre-built, managed connectors for databases, SaaS applications, file stores, and event streams, automating data extraction and loading.
  • Schema Drift Handling: Automatically detects and adapts to schema changes in source systems, propagating them to the destination without manual intervention.
  • Incremental Data Replication: Supports efficient data loading by only replicating new or changed data, minimizing resource consumption and latency.
  • Managed Transformations: Provides capabilities to transform data within the destination data warehouse after loading, often through integration with tools like dbt Core Fivetran dbt transformations.
  • Data Governance and Security: Includes features for data privacy, access control, and compliance with regulations such as SOC 2 Type II, GDPR, and HIPAA Fivetran security and compliance overview.
  • REST API: Allows programmatic creation, configuration, and management of connectors, destinations, and transformations, enabling integration with CI/CD pipelines and custom workflows Fivetran REST API reference.
  • Data Orchestration: Enables scheduling and monitoring of data pipelines, providing visibility into data freshness and pipeline health.

Pricing

Fivetran's pricing model is usage-based, primarily determined by Monthly Active Rows (MAR). MAR refers to the number of unique primary key rows that are updated or inserted in a customer's destination during a billing cycle. The cost varies based on the volume of MAR and the chosen service tier, which includes different features and support levels. A free tier is available for lower usage volumes.

Plan Name Description Key Features Pricing Basis (As of 2026-05-27)
Standard Free Plan Entry-level plan for small projects or evaluation. Up to 500k Monthly Active Rows (MAR), standard connectors. Free (up to 500k MAR)
Starter For growing teams, offering more MAR and support. From 500k MAR, standard connectors, basic support. Starts from $100/month (usage-based, see pricing page) Fivetran pricing page
Standard For teams requiring higher throughput and additional features. Increased MAR, priority support, advanced features. Custom (usage-based, higher MAR tiers)
Enterprise Designed for large organizations with complex data needs. Highest MAR limits, dedicated support, advanced security, custom features. Custom (contact sales)

Common integrations

Alternatives

  • Airbyte: An open-source data integration platform offering a wide range of connectors and customizable data pipelines.
  • Matillion: A cloud-native data transformation solution primarily focused on ETL for cloud data warehouses, offering a visual interface for building jobs.
  • Stitch Data: A cloud ELT service by Talend, providing automated data replication from various sources into data warehouses.
  • Talend: Offers a suite of data integration tools, including open-source and commercial versions, with broad capabilities for ETL and data management.
  • ETL tools from cloud providers: Services like AWS Glue AWS Glue documentation and Google Cloud Dataflow Google Cloud Dataflow reference provide managed ETL capabilities within their respective ecosystems, often requiring more configuration but offering deep integration.

Getting started

While Fivetran primarily operates through its web UI for initial connector setup, its REST API allows for programmatic management. This Python example demonstrates how to use the Fivetran API to create a new destination, assuming you have an API key and secret configured.

import requests
import json

# Replace with your actual Fivetran API key and secret
API_KEY = "YOUR_FIVETRAN_API_KEY"
API_SECRET = "YOUR_FIVETRAN_API_SECRET"

# Fivetran API base URL
BASE_URL = "https://api.fivetran.com/v1"

headers = {
    "Authorization": f"Basic {base64.b64encode(f'{API_KEY}:{API_SECRET}'.encode()).decode()}",
    "Content-Type": "application/json"
}

def create_destination(destination_name, destination_type, config_data):
    """
    Creates a new Fivetran destination.
    """
    url = f"{BASE_URL}/destinations"
    payload = {
        "group_id": "YOUR_GROUP_ID", # Replace with your Fivetran Group ID
        "service": destination_type, # e.g., "snowflake", "bigquery", "redshift"
        "region": "US_EAST_1", # Or your preferred region
        "time_zone_offset": "-5", # UTC offset, e.g., -5 for EST
        "config": config_data,
        "trust_certificates": False,
        "run_setup_tests": True
    }
    
    try:
        response = requests.post(url, headers=headers, data=json.dumps(payload))
        response.raise_for_status() # Raise an HTTPError for bad responses (4xx or 5xx)
        print("Destination created successfully:")
        print(json.dumps(response.json(), indent=2))
        return response.json()
    except requests.exceptions.HTTPError as err:
        print(f"HTTP error occurred: {err}")
        print(f"Response body: {response.text}")
    except Exception as err:
        print(f"An error occurred: {err}")
    return None

# Example: Creating a new Snowflake destination
if __name__ == "__main__":
    # Example Snowflake configuration (replace with actual values)
    snowflake_config = {
        "host": "your_snowflake_account.snowflakecomputing.com",
        "port": 443,
        "database": "YOUR_DATABASE_NAME",
        "schema": "FIVETRAN",
        "login_name": "YOUR_SNOWFLAKE_USER",
        "password": "YOUR_SNOWFLAKE_PASSWORD",
        "role": "SYSADMIN",
        "warehouse": "YOUR_WAREHOUSE_NAME"
    }
    
    # Remember to replace 'YOUR_GROUP_ID' with an actual Group ID from your Fivetran account
    # and provide valid Snowflake connection details.
    new_destination = create_destination(
        destination_name="my_new_snowflake_destination",
        destination_type="SNOWFLAKE",
        config_data=snowflake_config
    )

    if new_destination:
        print(f"Successfully created destination: {new_destination['data']['id']}")

Before running this code, ensure you have your Fivetran API key and secret, as well as a Fivetran Group ID, which can be found in your Fivetran dashboard. The base64 module is used for encoding the API key and secret for basic authentication. This script creates a new destination, and similar API calls can be used to manage connectors, transformations, and other Fivetran resources programmatically Fivetran REST API documentation.