Fivetran is a fully managed Extract, Load, Transform (ELT) platform that automates data movement from 700+ sources to cloud data warehouses, lakes, and analytics platforms with zero-maintenance pipelines. Unlike traditional ETL, Fivetran loads raw data first, then transforms it in the destination using tools like dbt, leveraging the compute power of modern warehouses. The platform's core value proposition is operational simplicity: automated schema migration, built-in connector maintenance, log-based change data capture, and consumption-based pricing measured in Monthly Active Rows (MAR)—rows that are inserted, updated, or deleted each month. Understanding Fivetran means recognizing the trade-off between managed convenience at a premium cost versus self-hosted flexibility, and knowing when its connector library, CDC capabilities, and enterprise compliance certifications justify the investment for your data engineering stack.
What This Cheat Sheet Covers
This topic spans 15 focused tables and 114 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.
Table 1: Connector Types and Categories
Connectors are the front door to Fivetran—each one knows how to extract from a specific kind of source, and the category tells you what extraction method and feature set to expect. Databases lean on log-based CDC, SaaS apps go through REST APIs, and the spectrum runs from fully-featured Standard connectors down to lightweight Lite ones and heavy-duty High-Volume Agents for enterprise databases.
| Type | Example | Description |
|---|---|---|
PostgreSQLMySQLSQL ServerOracle | • Replicate data from relational databases using log-based CDC or query-based methods • supports incremental sync, soft deletes, and schema drift handling | |
SalesforceHubSpotZendeskGoogle Analytics | • Extract data from business apps via REST APIs • handles rate limiting, pagination, and API version changes automatically | |
Amazon S3Google Cloud StorageAzure Blob Storage | • Ingest CSV, JSON, Avro, Parquet from cloud storage • supports unstructured file replication with metadata tracking for files up to 5 GB | |
KafkaAWS KinesisGoogle Pub/Sub | • Capture streaming data from message queues and event platforms • delivers near real-time ingestion for time-series and event-driven workloads |