Fivetran: Automated Data Integration and ELT Platform

Overview

Fivetran is an automated data integration provider offering a platform for Extract, Load, Transform (ELT) processes. The system is designed to centralize data from disparate operational sources into a single data warehouse or data lake for analytical purposes. Founded in 2012, Fivetran aims to reduce the manual effort associated with building and maintaining data pipelines by providing pre-built, managed connectors. These connectors are engineered to handle schema changes, data type conversions, and incremental data synchronization automatically, which can reduce the overhead for data engineering teams on how Fivetran operates.

The platform is suited for organizations that require reliable and consistent data replication from a variety of sources, including databases, cloud applications, and event streams. Fivetran's architecture separates the extraction and loading phases from the transformation phase, adhering to the ELT paradigm. This approach allows raw data to be loaded into the destination first, preserving its original structure, before transformations are applied. This can be beneficial for flexibility in analytical modeling and for regulatory compliance, as the original data remains accessible comparing ELT and ETL processes.

Fivetran's core offerings include a library of data connectors, capabilities for data transformations, and features for data governance. The data connectors automate the process of moving data from over 300 sources to various destinations, including popular data warehouses like Snowflake, Google BigQuery, and Amazon Redshift Fivetran's supported destinations. Transformations can be defined using SQL or dbt (data build tool) within the platform, allowing users to prepare data for specific analytical use cases. Data governance features include access controls, data lineage tracking, and compliance certifications such as SOC 2 Type II, GDPR, and HIPAA Fivetran security and compliance documentation.

The platform's design emphasizes data reliability and accuracy, with features like automatic re-syncs for historical data and idempotent data loading to prevent duplicates. This focus aims to ensure that the data available for business intelligence and machine learning applications is consistent and up-to-date, supporting data-driven decision-making processes within an organization. Fivetran provides a REST API and SDKs for Python, Go, Java, Ruby, and TypeScript/JavaScript, enabling programmatic management of connectors and transformations for integration into existing data infrastructure Fivetran REST API reference.

Key features

• Automated Data Connectors: Pre-built, managed connectors for over 300 data sources, including databases, SaaS applications, and event streams. These connectors automatically handle schema changes and data type conversions.
• ELT Architecture: Extracts and loads raw data into a destination before transformation, preserving original data and offering flexibility for downstream processing.
• Data Transformations: Supports SQL and dbt for in-platform data transformations, allowing users to clean, enrich, and model data for analytics.
• Data Governance: Includes features for access control, data lineage, and compliance with standards such as GDPR, HIPAA, and SOC 2 Type II.
• Reliable Data Replication: Ensures data consistency and accuracy through automatic re-syncs, idempotent loading, and robust error handling.
• REST API and SDKs: Provides a REST API for programmatic management of connectors, destinations, and transformations, with SDKs available for Python, Go, Java, Ruby, and TypeScript/JavaScript.
• Schema Drift Handling: Automatically detects and adapts to schema changes in source systems, reducing manual maintenance.

Pricing

Fivetran utilizes a usage-based pricing model, primarily calculated on Monthly Active Rows (MAR). The cost per MAR decreases at higher volumes. Below is a summary of the pricing structure as of May 2026.

For detailed pricing and specific volume discounts, refer to the official Fivetran pricing page.

Common integrations

• Data Warehouses:
  • Snowflake
  • Google BigQuery
  • Amazon Redshift
  • Databricks
  • Azure Synapse Analytics
• Databases:
  • PostgreSQL
  • MySQL
  • SQL Server
  • Oracle
  • MongoDB
• SaaS Applications:
  • Salesforce
  • NetSuite
  • ServiceNow
  • HubSpot
  • Stripe
• Marketing & Analytics:
  • Google Ads
  • Google Analytics 4
  • Facebook Ads
  • LinkedIn Ads

Alternatives

• Matillion: Offers cloud-native ETL/ELT solutions with a strong focus on data transformation and orchestration within data warehouses.
• Stitch Data: A cloud-agnostic ELT platform that provides data replication from various sources to data warehouses.
• Talend: Provides a suite of data integration tools, including ETL, data quality, and master data management, available in open-source and commercial editions.
• Airbyte: An open-source data integration platform that allows users to build and manage custom connectors with a focus on flexibility and extensibility.
• Informatica: Offers a broad range of enterprise data management solutions, including cloud data integration, data quality, and data governance.

Getting started

To get started with Fivetran, you can use the REST API to manage connectors. The following Python example demonstrates how to create a new connector using the Fivetran API. This assumes you have your Fivetran API key and secret set as environment variables or replaced directly.

import requests
import os

# Replace with your Fivetran API Key and Secret
API_KEY = os.getenv('FIVETRAN_API_KEY')
API_SECRET = os.getenv('FIVETRAN_API_SECRET')

if not API_KEY or not API_SECRET:
    raise ValueError("Fivetran API Key and Secret must be set as environment variables.")

# Fivetran API endpoint for creating a connector
url = "https://api.fivetran.com/v1/connectors"

# Example payload for creating a PostgreSQL connector
# This is a minimal example; actual configuration will be more detailed
payload = {
    "service": "postgres",
    "group_id": "your_group_id", # Replace with your Fivetran Group ID
    "schema": "your_schema_name", # Replace with your desired schema name
    "config": {
        "host": "your_db_host",
        "port": 5432,
        "database": "your_db_name",
        "user": "your_db_user",
        "password": "your_db_password",
        "connection_type": "Direct", # or "SSH_TUNNEL", "AWS_PRIVATELINK", etc.
        "is_fivetran_managed": False # Set to True if Fivetran manages the destination
    }
}

headers = {
    "Content-Type": "application/json"
}

# Make the API request
try:
    response = requests.post(url, json=payload, headers=headers, auth=(API_KEY, API_SECRET))
    response.raise_for_status() # Raise an exception for HTTP errors (4xx or 5xx)

    connector_data = response.json()
    print("Connector created successfully:")
    print(f"Connector ID: {connector_data['data']['id']}")
    print(f"Service: {connector_data['data']['service']}")
    print(f"Schema: {connector_data['data']['schema']}")

except requests.exceptions.HTTPError as http_err:
    print(f"HTTP error occurred: {http_err}")
    print(f"Response content: {response.json()}")
except Exception as err:
    print(f"An error occurred: {err}")

Before running this code, ensure you have a Fivetran account, a Group ID, and the necessary database connection details. You will need to install the requests library (pip install requests). For a complete list of configuration parameters for different connector types, refer to the Fivetran REST API documentation.