Fivetran is a fully managed data integration platform that automates moving data from source systems into analytical destinations. It handles the Extract and Load steps of ELT -- pulling data from SaaS applications, databases, event streams, files, and APIs, then loading it into a warehouse, data lake, or operational database in a normalized, ready-to-transform schema. The pitch: configure a connector, provide credentials, and Fivetran takes care of the rest -- initial historical sync, incremental updates, schema migration, and error recovery.
Founded in 2012 by George Fraser and Taylor Brown, Fivetran grew from the observation that data engineers spent most of their time building and fixing connectors rather than doing analytical work. It has since become one of the most widely adopted data integration platforms, serving thousands of organizations including DocuSign, ClassPass, and The Trade Desk. Over 500 pre-built connectors, billions of rows processed daily.
How Fivetran Works
Fivetran operates on a three-step model: connect, sync, maintain.
Connect. Select a data source, provide authentication, choose which tables or objects to replicate. For databases, Fivetran uses log-based CDC where possible, or falls back to incremental key-based queries. For SaaS sources like Salesforce, HubSpot, or Stripe, it uses the vendor's API.
Sync. The initial sync is a full historical load. Subsequent syncs are incremental -- only changed records. Frequency is configurable: every five minutes to every 24 hours depending on plan and connector type. Fivetran maintains a cursor per table for efficient resumption after interruptions.
Automated schema migration. One of Fivetran's most valued features. When a column is added, renamed, or its type changes in the source, Fivetran detects the change and propagates it to the destination schema. This eliminates a common class of pipeline failures where upstream changes break downstream models. All schema changes are logged, with optional notifications before applying.
Incremental sync and CDC. For supported databases (PostgreSQL, MySQL, SQL Server, Oracle, MongoDB), Fivetran reads the transaction log for near real-time change capture, minimizing source load. Deletes are captured -- something timestamp-based incremental queries miss. For sources without log access, it falls back to querying for recently modified rows.
Key Features
500+ Pre-Built Connectors. Databases, SaaS applications (Salesforce, Google Analytics, Zendesk, Shopify, Jira, and hundreds more), file systems, event streams, webhooks. Connectors are fully managed and updated when source APIs change.
Zero Maintenance Pipelines. Once configured, connectors run without intervention. API changes, pagination, rate limiting, retries, and token refresh are all handled by the platform.
Schema Drift Handling. Automatic adaptation to source schema changes -- new columns added, renamed columns tracked, type changes handled gracefully. Configurable: block, allow, or notify.
Data Reliability. Row-level logging, data delay alerts, and a status dashboard for sync health across connectors. Data integrity guarantees and the ability to re-sync historical data when needed.
Transformations. Built-in dbt Core integration. Trigger dbt models to run automatically after syncs complete, keeping transformed models current.
Security and Compliance. SOC 2 Type II certified, HIPAA compliant. Column-level hashing for PII masking, TLS encryption in transit, and private networking options (AWS PrivateLink, Azure Private Link, GCP Private Service Connect) for enterprise customers.
Pricing
Fivetran uses consumption-based pricing centered on Monthly Active Rows (MAR). A MAR is any row inserted, updated, or deleted in your destination during the billing period. Unchanged rows don't count.
Plan tiers: Free for evaluation; Starter for small teams; Standard adds more connectors, faster sync frequencies, and dbt integration; Enterprise includes private networking, SLAs, and advanced security; Business Critical provides the highest data protection and compliance capabilities.
Cost can escalate with data volume, especially for high-churn tables where many rows change each month. Teams should evaluate which tables need frequent syncing, disable unnecessary connectors, and consider sync frequency carefully -- every five minutes consumes significantly more MAR than every six hours. For large deployments, negotiating volume-based contracts directly with Fivetran is common.
Fivetran Destinations
Cloud data warehouses are the most common targets -- Snowflake, BigQuery, Redshift, and Databricks are all first-class destinations.
For organizations working with search and analytics infrastructure, Fivetran also supports Elasticsearch, OpenSearch, and ClickHouse:
Elasticsearch and OpenSearch. Keep search indices synchronized with upstream data sources without building custom indexing pipelines. Valuable for e-commerce search, log analytics, and making data from multiple SaaS tools searchable.
ClickHouse. Funnel data from hundreds of sources into ClickHouse for real-time analytical queries. A strong combination for product analytics, financial reporting, and operational dashboards needing sub-second performance over fresh data.
Optimizing the destination side -- tuning index mappings in Elasticsearch, configuring table engines and sort keys in ClickHouse, managing shard allocation in OpenSearch -- requires specialized expertise.
Open-Source Alternatives
Airbyte is the most prominent open-source alternative. It offers 600+ connectors, both self-hosted and cloud-managed deployments, and a CDK for building custom connectors in Python. Self-hosting eliminates per-row pricing and gives full data residency control, which can be cost-effective at scale. The tradeoff is operational overhead -- you manage infrastructure, upgrades, and connector issues. Airbyte Cloud offers a managed experience with usage-based pricing that some teams find more predictable.
Other alternatives include Meltano (open-source, Singer-based), Stitch (SaaS, now part of Qlik), and Hevo Data. For database-specific replication, Debezium provides open-source CDC streaming through Kafka.
Common Use Cases
- Centralizing SaaS data for analytics. Pull data from dozens of SaaS tools -- CRM, marketing automation, support, billing, product analytics -- into a single warehouse for cross-source querying.
- Feeding search and analytics engines. Replicate transactional and operational data into Elasticsearch, OpenSearch, or ClickHouse, keeping these systems synchronized without custom ETL.
- Enabling ELT with dbt. The canonical modern data stack pattern: Fivetran loads raw data, dbt transforms it into clean models, a BI tool queries the results.
- Database replication and migration. CDC-based connectors replicate databases continuously -- useful for read replicas on different platforms, legacy migrations, or warehouse copies of operational data.
- Regulatory and compliance reporting. Financial services, healthcare, and other regulated industries use Fivetran to maintain consolidated, auditable data stores for reporting and audits across operational systems.