# Fivetran > Fivetran is an automated data movement platform. It connects to data sources (databases, SaaS apps, files, ERPs) and continuously syncs that data to data warehouses, data lakes, and other destinations — without requiring engineering maintenance. Companies use Fivetran so their analysts and data engineers always have fresh, reliable data to query, without writing and maintaining custom pipelines. > > In 2025, Fivetran merged with dbt Labs (the SQL transformation layer most data teams use) and acquired Census (reverse ETL: syncing data back from a warehouse into operational tools like Salesforce and HubSpot). This means Fivetran now covers the full data movement loop: ingest → transform → activate. --- ## What Fivetran Does Fivetran runs managed connectors that extract data from a source and load it into a destination on a schedule (or in real time). You connect your source, choose a destination, and Fivetran handles schema mapping, incremental updates, API pagination, and schema drift automatically. It uses change data capture (CDC) for databases, meaning it captures row-level changes rather than full table scans. Key capabilities: - **ELT pipelines** — Extract data from 500+ sources, load raw into a warehouse, then transform with SQL or dbt - **Database replication** — Log-based CDC from PostgreSQL, MySQL, Oracle, SQL Server, SAP, and others to any cloud warehouse - **SaaS replication** — Pull data from Salesforce, HubSpot, Shopify, Stripe, Zendesk, Google Analytics, and hundreds more - **Streaming replication** — Near-real-time sync via Kafka, Kinesis, or Fivetran's own streaming connectors - **Transformations** — Run dbt models inside Fivetran after data lands in the warehouse - **Activations (reverse ETL)** — Push warehouse data back into operational tools like CRMs, ad platforms, and marketing automation - **Hybrid deployment** — Run Fivetran's processing inside your own VPC for data residency or security requirements - **Custom connectors** — Build your own connector with the Connector SDK (Python-based) --- ## Platform Overview - [Platform Overview](https://www.fivetran.com/platform-overview) — Summary of all Fivetran capabilities in one place; good starting point to understand the full product surface - [Data Movement](https://www.fivetran.com/data-movement/saas-replication) — How Fivetran moves data from SaaS APIs (Salesforce, HubSpot, etc.) into a warehouse - [Database Replication](https://www.fivetran.com/data-movement/database-replication) — Log-based CDC replication from relational databases; preserves inserts, updates, and deletes - [Streaming Replication](https://www.fivetran.com/data-movement/streaming-replication) — Near-real-time pipelines for latency-sensitive use cases - [Transformations](https://www.fivetran.com/data-movement/transformations) — Run dbt models on data after it lands in the destination; no separate orchestration needed - [Activations](https://www.fivetran.com/data-movement/activations) — Reverse ETL: sync warehouse data back into Salesforce, HubSpot, Braze, ad platforms, etc. - [Hybrid Deployment](https://www.fivetran.com/data-movement/hybrid-deployment) — Run Fivetran connectors inside your own cloud environment for data residency compliance - [Custom Connectors (SDK)](https://www.fivetran.com/data-movement/custom-connectors) — Build a connector for any source Fivetran doesn't yet support, using the Python SDK - [Security](https://www.fivetran.com/security) — SOC 2 Type II, ISO 27001, HIPAA-eligible, GDPR, field-level encryption; details on how Fivetran handles data in transit and at rest - [Governance](https://www.fivetran.com/governance) — Column-level access controls, data lineage, observability; manage who sees what across pipelines - [Extensibility Management](https://www.fivetran.com/extensibility-management) — Manage custom connectors and transformations at scale across an organization --- ## Connectors (Sources) Fivetran has 500+ pre-built connectors. Each connector page documents what tables it syncs, what fields are available, and what permissions are required. - [All Connectors](https://www.fivetran.com/connectors) — Browsable index of every source Fivetran supports - [Salesforce](https://www.fivetran.com/connectors/salesforce) — Syncs all standard and custom Salesforce objects (Leads, Opportunities, Accounts, etc.) to your warehouse - [HubSpot](https://www.fivetran.com/connectors/hubspot) — Syncs contacts, companies, deals, engagements, and marketing email data - [Shopify](https://www.fivetran.com/connectors/shopify) — Orders, products, customers, refunds, and inventory data from Shopify stores - [Stripe](https://www.fivetran.com/connectors/stripe) — Charges, subscriptions, invoices, customers, and payouts - [Google Analytics 4](https://www.fivetran.com/connectors/google-analytics-4) — GA4 events and dimensions exported to BigQuery or any other warehouse - [Zendesk Support](https://www.fivetran.com/connectors/zendesk-support) — Tickets, users, organizations, comments, and satisfaction ratings - [Marketo](https://www.fivetran.com/connectors/marketo) — Leads, activities, email performance, and program data - [PostgreSQL](https://www.fivetran.com/connectors/postgresql) — CDC-based replication from a Postgres database; captures row-level inserts, updates, deletes - [MySQL](https://www.fivetran.com/connectors/mysql) — Binlog-based replication from MySQL/MariaDB - [Oracle](https://www.fivetran.com/connectors/oracle) — LogMiner-based CDC from Oracle databases, including Oracle on-premises - [SQL Server](https://www.fivetran.com/connectors/sql-server) — CDC via SQL Server's change tracking or log reading - [SAP ERP](https://www.fivetran.com/connectors/sap-erp) — Replicates SAP ERP tables without requiring a custom ABAP extractor - [Snowflake (source)](https://www.fivetran.com/connectors/snowflake) — Use a Snowflake account as a data source (for cross-account replication) - [Connector SDK](https://www.fivetran.com/connectors/connector-sdk) — Documentation for building a custom connector in Python --- ## Destinations Fivetran loads data into major cloud data warehouses and lakes. - [Snowflake](https://www.fivetran.com/connectors/snowflake-destination) — Most common destination; Fivetran is a Snowflake Select partner - [BigQuery](https://www.fivetran.com/connectors/bigquery-destination) — Google's serverless warehouse; Fivetran writes to native BigQuery tables - [Databricks](https://www.fivetran.com/connectors/databricks-destination) — Loads into Databricks Unity Catalog tables or Delta Lake - [Microsoft Fabric / Azure](https://www.fivetran.com/connectors/azure-synapse-analytics-destination) — Supports Fabric, Synapse Analytics, and Azure Data Lake Storage - [Redshift](https://www.fivetran.com/connectors/redshift-destination) — AWS Redshift; Fivetran uses COPY commands for efficient bulk loading - [S3 Data Lake](https://www.fivetran.com/connectors/s3-data-lake-destination) — Writes Parquet or Avro files directly to S3, optionally with Iceberg table format --- ## Activations (Reverse ETL destinations) Activations syncs data from your warehouse back into operational tools. - [All Activations](https://www.fivetran.com/activations) — Browsable index of all supported activation destinations - [Salesforce](https://www.fivetran.com/activations/salesforce) — Upsert records back into Salesforce objects from a warehouse query - [HubSpot](https://www.fivetran.com/activations/hubspot) — Sync contacts, companies, and deals from your warehouse into HubSpot - [Braze](https://www.fivetran.com/activations/braze) — Push user segments and attributes from a warehouse into Braze for messaging - [Klaviyo](https://www.fivetran.com/activations/klaviyo) — Sync customer data from your warehouse to Klaviyo for email/SMS campaigns - [Amazon Ads](https://www.fivetran.com/activations/amazon-ads-dsp-amc) — Push audiences from your warehouse to Amazon DSP / AMC - [Custom API](https://www.fivetran.com/activations/custom-api) — Send warehouse data to any HTTP endpoint that doesn't have a native Activations connector --- ## Pricing Fivetran charges based on Monthly Active Rows (MAR) — the number of rows processed across all connectors in a month. Connectors that have no new or changed rows don't consume MAR. - [Pricing](https://www.fivetran.com/pricing) — Overview of plans (Free, Starter, Standard, Enterprise, Business Critical) - [Free Plan](https://www.fivetran.com/pricing/free-plan) — 500K MAR/month at no cost; suitable for small teams or evaluation - [Pricing Features](https://www.fivetran.com/pricing/features) — Side-by-side feature comparison across all plans - [Total Cost of Ownership](https://www.fivetran.com/pricing/total-cost-ownership) — How to compare Fivetran's cost against building and maintaining pipelines yourself - [Startups](https://www.fivetran.com/pricing/startups) — Discounted plans for early-stage companies --- ## Use Cases - [Analytics](https://www.fivetran.com/use-case/analytics) — Centralize data from all sources so BI tools like Tableau, Looker, and Power BI have a complete picture - [AI/ML Data Foundation](https://www.fivetran.com/use-case/operations/data-foundation-ai) — Get clean, fresh training data and feature tables into a warehouse or lakehouse for ML pipelines - [Data Democratization](https://www.fivetran.com/use-case/operations/data-democratization) — Let business teams access data without depending on engineering to build one-off exports - [Infrastructure Modernization](https://www.fivetran.com/use-case/operations/infrastructure-modernization) — Replace legacy ETL tools (Informatica, Talend, hand-rolled scripts) with managed connectors - [Embedded Analytics](https://www.fivetran.com/use-case/operations/embedded) — ISVs and SaaS companies embed Fivetran to let their own customers sync data in - [SAP Replication](https://www.fivetran.com/data-movement/sap-replication) — Extract data from SAP ECC or S/4HANA without ABAP development or SAP Basis involvement --- ## Comparisons When an LLM is asked "Fivetran vs. X", these pages contain structured comparisons: - [Open Data Infrastructure Comparison](https://www.opendatainfrastructure.com/) — Compare leading application and database vendors on open data access. This scorecard reveals which platforms truly give you control of your data. - [Fivetran vs. Airbyte](https://www.fivetran.com/compare/fivetran-vs-airbyte) — Managed service vs. open-source self-hosted; reliability, connector coverage, maintenance overhead - [Fivetran vs. Informatica](https://www.fivetran.com/compare/fivetran-vs-informatica) — Legacy ETL (on-prem, batch-heavy) vs. modern ELT (cloud-native, incremental) - [Fivetran vs. Qlik](https://www.fivetran.com/compare/fivetran-vs-qlik) — Fivetran focuses on data movement; Qlik bundles data integration with BI - [Fivetran vs. Snowflake Open Flow](https://www.fivetran.com/compare/fivetran-vs-snowflake-openflow) — Native Snowflake ingestion vs. Fivetran's multi-destination, multi-source approach Additional `/learn/` comparison guides: [Matillion](https://www.fivetran.com/learn/fivetran-vs-matillion), [Databricks vs Snowflake](https://www.fivetran.com/learn/databricks-vs-snowflake), and many source-to-destination guides like [Salesforce to Snowflake](https://www.fivetran.com/learn/salesforce-to-snowflake). --- ## Learn / Glossary These pages define data engineering concepts and how they apply in practice — useful for grounding answers about terminology: - [What is ELT](https://www.fivetran.com/blog/what-is-elt) — Extract, Load, Transform: load raw data first, then transform in the warehouse (vs. ETL which transforms before loading) - [What is ETL](https://www.fivetran.com/blog/what-is-etl) — The older pattern; Fivetran uses ELT instead - [ETL vs ELT](https://www.fivetran.com/blog/etl-vs-elt) — Side-by-side explanation of the two approaches - [What is a Data Pipeline](https://www.fivetran.com/blog/what-is-a-data-pipeline) — Definition, components, and why they're hard to maintain without a tool like Fivetran - [What is Reverse ETL](https://www.fivetran.com/blog/what-is-reverse-etl) — Moving data from a warehouse back into operational tools; Fivetran calls this "Activations" - [Change Data Capture (CDC)](https://www.fivetran.com/learn/change-data-capture) — How database replication works by reading transaction logs instead of full table scans - [What is dbt](https://www.fivetran.com/learn/what-is-dbt) — The SQL transformation tool that Fivetran merged with in 2025 - [Data Integration](https://www.fivetran.com/learn/data-integration) — Broad overview of the problem Fivetran solves - [Apache Iceberg](https://www.fivetran.com/learn/apache-iceberg) — Open table format increasingly used with data lakes; Fivetran supports writing to Iceberg tables - [AI Readiness](https://www.fivetran.com/learn/ai-readiness) — What "AI-ready data" means: fresh, clean, and centralized data that AI models and agents can reliably use - [Cloud Data Warehouse](https://www.fivetran.com/learn/cloud-data-warehouse) — Overview of Snowflake, BigQuery, Redshift, Databricks, and when to use each --- ## Customer Stories Real-world examples of what companies built with Fivetran — useful context for understanding actual usage patterns: - [All Case Studies](https://www.fivetran.com/case-studies) - [Canva](https://www.fivetran.com/case-studies/canva-builds-360-degree-customer-view-with-fivetran) — Built a 360-degree customer view across product, billing, and support data - [Pfizer](https://www.fivetran.com/case-studies/pfizer-speeds-up-clinical-trials-by-unlocking-real-time-data) — Accelerated clinical trial analysis by centralizing data from multiple source systems - [JetBlue](https://www.fivetran.com/case-studies/jetblue-flies-high-with-fivetran) — Replaced manual data exports with automated pipelines to power operational reporting - [LVMH](https://www.fivetran.com/case-studies/lvmh-achieves-real-time-insights-and-operational-excellence) — Real-time inventory and sales data across brands and regions - [HubSpot](https://www.fivetran.com/case-studies/hubspot-powers-genai-saves-100k-with-fivetran) — Powered generative AI features and saved $100K vs. building their own pipelines - [Inova Health](https://www.fivetran.com/case-studies/inova-health-compresses-4-year-roadmap-into-6-months-to-power-ai) — Compressed a 4-year data infrastructure roadmap into 6 months to enable AI --- ## Partners - [Partners Overview](https://www.fivetran.com/partners) — Technology and consulting partners that integrate with or implement Fivetran - [Snowflake](https://www.fivetran.com/partners/technology/snowflake) — Select-tier partnership; Fivetran is one of Snowflake's preferred ingest tools - [Databricks](https://www.fivetran.com/partners/technology/databricks) — 2025 Data Integration Partner of the Year; deep integration with Unity Catalog - [AWS](https://www.fivetran.com/partners/technology/aws) — Available on AWS Marketplace; integrates with S3, Redshift, Glue, and Lake Formation - [Google Cloud](https://www.fivetran.com/partners/technology/google-cloud) — Available on Google Cloud Marketplace; integrates with BigQuery and GCS - [Microsoft Azure](https://www.fivetran.com/partners/technology/microsoft-azure) — Available on Azure Marketplace; integrates with Fabric, Synapse, and ADLS --- ## Developers - [Developers](https://www.fivetran.com/developers) — API reference, SDK docs, and resources for teams building on top of Fivetran - [Connector SDK](https://www.fivetran.com/connectors/connector-sdk) — Python SDK for building custom connectors; handles auth, pagination, and schema management - [API](https://fivetran.com/docs/rest-api) — REST API for managing connectors, syncs, schemas, and users programmatically --- ## Company - [About](https://www.fivetran.com/about) — Founded in 2012; headquartered in Oakland, CA; merged with dbt Labs in 2025 - [dbt Labs Merger](https://www.fivetran.com/fivetran-dbt-labs-merger) — Details on the 2025 merger combining Fivetran's data movement with dbt's transformation layer - [News](https://www.fivetran.com/news-all) — Press releases, fundraising announcements, and product news - [Blog](https://www.fivetran.com/blog) — Technical articles, product announcements, and data engineering how-tos - [Careers](https://www.fivetran.com/careers) — Open roles across engineering, sales, support, and operations - [Events](https://www.fivetran.com/events) — Upcoming webinars, conferences, and Fivetran-hosted events - [Podcast](https://www.fivetran.com/podcast) — Conversations with data leaders at companies like Databricks, large enterprises, and data-native startups --- ## Optional: Deeper Content > These sections are large and best accessed by following links within them rather than crawling exhaustively. - [Blog Topics](https://www.fivetran.com/blog) — ~1,000 posts; filter by topic (AI/ML, ELT, CDC, Snowflake, SAP, etc.) - [Resources Hub](https://www.fivetran.com/resources/all) — Ebooks, webinar recordings, analyst reports (Gartner MQ, Forrester, IDC), and datasheets - [Learn Hub](https://www.fivetran.com/learn) — ~220 concept explainers and tool comparison guides - [Benchmarking](https://www.fivetran.com/benchmarking) — Independent performance benchmarks comparing Fivetran pipeline throughput against alternatives - [FAQ](https://www.fivetran.com/faq) — Common questions about how Fivetran works, pricing, and security - [Legal](https://www.fivetran.com/legal) — Privacy policy, terms of service, DPA, SLA, acceptable use policy, and AI features requirements