Fivetran Managed ELT Cheat Sheet

Back to Data EngineeringUpdated 2026-05-15

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 CategoriesTable 2: Sync Frequency and SchedulingTable 3: Historical Sync and Initial LoadTable 4: Incremental Replication MethodsTable 5: Fivetran Transformations and dbt IntegrationTable 6: Normalized Schema Output and Table NamingTable 7: Pricing Plans and TiersTable 8: Connector Health Monitoring and AlertsTable 9: Fivetran REST API for AutomationTable 10: Destination Setup for Major PlatformsTable 11: Privacy, Security, and Data ProtectionTable 12: Hybrid Deployment and Private DeploymentTable 13: Census Acquisition and Reverse ETLTable 14: Advanced Features and OptimizationsTable 15: Common Use Cases and Anti-Patterns

Table 1: Connector Types and Categories

Type	Example	Description
Database connectors	`PostgreSQL` `MySQL` `SQL Server` `Oracle`	Replicate data from relational databases using log-based CDC or query-based methods; supports incremental sync, soft deletes, and schema drift handling.
SaaS application connectors	`Salesforce` `HubSpot` `Zendesk` `Google Analytics`	Extract data from business apps via REST APIs; handles rate limiting, pagination, and API version changes automatically.
File connectors	`Amazon S3` `Google Cloud Storage` `Azure Blob Storage`	Ingest CSV, JSON, Avro, Parquet from cloud storage; supports unstructured file replication with metadata tracking for files up to 5 GB.
Event stream connectors	`Kafka` `AWS Kinesis` `Google Pub/Sub`	Capture streaming data from message queues and event platforms; delivers near real-time ingestion for time-series and event-driven workloads.

Table 1: Connector Types and Categories

Type	Example	Description
Database connectors	`PostgreSQL` `MySQL` `SQL Server` `Oracle`	Replicate data from relational databases using log-based CDC or query-based methods; supports incremental sync, soft deletes, and schema drift handling.
SaaS application connectors	`Salesforce` `HubSpot` `Zendesk` `Google Analytics`	Extract data from business apps via REST APIs; handles rate limiting, pagination, and API version changes automatically.
File connectors	`Amazon S3` `Google Cloud Storage` `Azure Blob Storage`	Ingest CSV, JSON, Avro, Parquet from cloud storage; supports unstructured file replication with metadata tracking for files up to 5 GB.
Event stream connectors	`Kafka` `AWS Kinesis` `Google Pub/Sub`	Capture streaming data from message queues and event platforms; delivers near real-time ingestion for time-series and event-driven workloads.