Data Connector
A Data Connector is a integration component that links a platform or application to external data sources (databases, APIs, SaaS systems, file stores) enabling data movement and querying without requiring native drivers.
Data Connectors are broader in scope than Database Connectors. While database connectors specifically connect to databases via JDBC, ODBC, or similar protocols, data connectors encompass connections to APIs (Salesforce, Slack, Google Analytics), cloud storage (S3, Azure Blob Storage), SaaS systems (HubSpot, Zendesk), and file formats (CSV, Parquet). Data Connectors translate between the conventions of the source system and the platform consuming the data.
A data connector for an API must understand the API's authentication (OAuth, API keys), pagination (how to retrieve large datasets in chunks), rate limiting (how fast to request data), and response format (JSON, XML). A connector for cloud storage must handle credentials, object listing, and parallel file transfer. This abstraction is essential for platforms serving diverse users: rather than requiring analysts to write custom code to fetch data from each SaaS system, data connectors provide point-and-click configuration.
Data connectors are fundamental to modern data integration platforms like Fivetran, Stitch, and cloud-native tools. They are also central to data orchestration, enabling platforms like Airflow or Dagster to exchange data with hundreds of systems. Connectors can be built-in (shipped with the platform), third-party (from ecosystem vendors), or custom (built for proprietary systems).
Key Characteristics
- ▶Connects to diverse data sources including APIs, databases, cloud storage, and SaaS systems
- ▶Handles source-specific authentication (OAuth, API keys, certificates, credentials)
- ▶Manages pagination, rate limiting, and retries to handle large and throttled data retrieval
- ▶Transforms source data formats (JSON, CSV, XML) into the platform's native format
- ▶May include incremental loading (fetching only changed data) to reduce bandwidth and latency
- ▶Provides configuration interfaces allowing users to specify which data to fetch without code
Why It Matters
- ▶Eliminates custom coding for data integration by providing pre-built connectivity to common sources
- ▶Enables self-service data access where analysts configure connectors without engineering intervention
- ▶Reduces time and cost of bringing external data into analytics environments
- ▶Centralizes authentication and compliance controls across all external data sources
- ▶Facilitates data pipeline orchestration by enabling systems to seamlessly connect multiple data sources
- ▶Supports real-time or incremental data synchronization reducing storage and computational costs
Example
A Fivetran user wants to ingest Salesforce data into Snowflake. They configure the Salesforce connector with API credentials and select which objects (accounts, opportunities, contacts) to sync. Fivetran's connector handles Salesforce's OAuth authentication, manages incremental updates, and loads data into Snowflake tables automatically.
Coginiti Perspective
Coginiti supports 24+ database connectors (including cloud platforms, enterprise databases, and generic JDBC), enabling seamless connections to diverse data sources without requiring custom drivers. Once connected, Coginiti's publication framework enables materialization to any supported target platform, and Coginiti Actions enables scheduled data movement through these connections. The semantic layer (SMDL) can be built on top of connected data sources, providing consistent business definitions regardless of source system, while query tags enable monitoring and cost allocation across multi-platform deployments.
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