Connector maintenance is a structural tax on every data team. Source application programming interfaces (APIs) change, schemas shift, and tokens expire, and each event lands on the engineers who own the pipelines. Managed connectors move that upkeep off the team's payroll, and the trade-off against building in-house comes down to source volatility and engineering capacity. With CData Sync, hundreds of pre-built, actively maintained connectors absorb that work.
What are data connectors in ETL?
A data connector is a reusable software component that establishes and manages the connection between a source system and a destination, so data moves automatically through an extract, transform, load (ETL) or extract, load, transform (ELT) pipeline. In ETL, data connectors handle the extract and load phases, retrieving records from a source system and pushing them into a destination such as a data warehouse, while transformation happens separately in the tool or the destination, as IBM describes.
ETL transforms data before loading, and ELT loads raw data first and transforms it in the destination. Connectors perform the same extract and load work in both patterns, so connector maintenance practices apply equally to ETL and ELT. CData's overview of data connectors covers the main connector types and how each fit into a pipeline.
Common types of ETL data connectors
Connector type | What it connects to | Typical example |
Database | Relational and NoSQL databases | SQL Server, Oracle, MySQL |
API or web service | Representational State Transfer (REST) and Simple Object Access Protocol (SOAP) endpoints | Internal microservices |
File-based | Flat files and object storage | Amazon S3, Azure Data Lake Storage, Google Cloud Storage |
Cloud software as a service (SaaS) application | Business applications | Salesforce, Workday |
Reverse ETL | Operational applications as destinations | Customer relationship management (CRM) platforms |
Failure modes and root causes behind broken ETL connectors
Two root causes drive most connector breakage: source API version changes and schema drift. Microsoft Learn explains why rigid source mappings fail when schemas change.
Schema drift is when the structure of source data, including new, renamed, dropped, or retyped fields, changes unexpectedly in ways the pipeline's design did not anticipate. The most dangerous form fails silently. A renamed source field, for example, can let a sync finish green while a revenue field loads null for days, leaving dashboards wrong until someone notices.
API changes cause the second failure mode. SaaS vendors add, rename, or reformat fields in new API versions, and connectors with mappings tied to specific source names break when upstream sources evolve without notice. Vendors such as Salesforce publish new API versions on a regular cadence, so mappings need regular review. Ad hoc drift handling stops scaling once several sources feed the same warehouse, because each added source multiplies the odds of an undetected drift event.
How to monitor connector health and detect issues early
Monitoring tells a team the pipeline ran, while observability tells whether it produced correct, complete, and fresh data. A pipeline can report success and still deliver zero rows, which is why the job-ran signal alone cannot establish trust in a connector.
Data freshness is often the first signal that a connector silently stopped. CData Sync detects source schema changes on each run.
Signal | What it catches | Example alert |
Freshness lag | Connector stalled or delayed | Object older than its expected update interval |
Schema drift | Renamed, dropped, or retyped fields | Source schema differs from stored baseline |
Row volume | Partial loads or empty runs | Row count falls outside expected range |
A step-by-step workflow to update and test connectors
When an API change or drift event forces a fix, a repeatable workflow keeps the update predictable: detect the trigger, review the changelog, update mappings, test in staging, and verify after deploying.
Detect and triage: Confirm the trigger, whether a deprecation notice, drift alert, or failed run, and rank affected fields by criticality. Critical fields get hard alerts, and optional fields get soft warnings.
Review the changelog: Read the source's deprecation notice to identify which fields, endpoints, or parameters changed. Breaking changes typically remove or rename one of the three.
Update mappings and auth: Revise field mappings and handle authentication changes explicitly, since expired tokens and Open Authorization (OAuth) grant updates commonly break connectors.
Test in staging: Run contract tests and canary runs. A contract test validates that incoming data matches an agreed schema before a change deploys, which catches drift in continuous integration (CI) or staging instead of production.
Deploy and verify: Confirm freshness and row counts return to baseline and treat pre-deploy testing as mandatory, since a version change can alter every mapping at once.
Vendor Sunset headers and deprecation dates let teams schedule updates ahead of removal instead of reacting after breakage. Managed vendors publish the same information in release notes, as in the CData Sync Q3 2025 release post.
Should ETL teams build or buy connectors?
The build is the cheap part, and the bulk of the work is maintenance. Six months to build one performant connector, and up to two years to cover complex sources such as Workday or NetSuite, according to CData's build vs. buy analysis. With CData Sync, connection-based pricing keeps costs predictable as data volume grows.
On the build side, every source API or schema change forces the internal team to re-engineer the integration, while managed vendors provide connectors that stay current.
Factor | Lean build | Lean buy |
Source API stability | Stable internal system | Frequently changing SaaS API |
Engineering capacity | Dedicated team with spare cycles | Stretched team with a growing backlog |
Source volatility | Few schema changes | Frequent field and version changes |
Compliance control | Code-level ownership required | Vendor certifications meet requirements |
CData Sync represents the buy path, with hundreds of pre-built, actively maintained connectors. One example is the Workday integration. A hybrid model buys managed connectors for volatile third-party SaaS sources and builds for a small set of proprietary internal systems. CData's guides to choosing ETL with dbt and real-time ETL tools cover adjacent decisions.
Maintain reliable ETL connectors with CData Sync
With CData Sync, schema drift handling, authentication updates, and version compatibility are the vendor’s job, so engineers stop patching deprecated endpoints connector by connector. Start a free trial of CData Sync to see managed connectors at work.
Frequently asked questions
What are the different types of data connectors in ETL?
Common types include database, API or web service, file-based, cloud SaaS application, and reverse ETL connectors. Each type links a specific kind of source or destination to the pipeline.
How do you update an ETL connector when a source API changes?
Review the changelog, update field mappings and authentication, test in staging with contract and canary runs, then deploy and verify that freshness and row counts return to baseline.
What are the most common reasons ETL data connectors fail?
Source API version changes, schema drift, and expired or changed authentication cause most failures. Rigid field mappings turn each of these into a broken or silently incorrect load.
What tools do you need before you can maintain data connectors in ETL?
Maintaining data connectors in ETL requires freshness and row-volume monitoring, schema drift detection against a stored baseline, a staging environment, and contract tests.
Should you build your own ETL connectors or use managed connectors?
Build for niche or internal systems with stable interfaces and buy for frequently changing SaaS sources. Many teams combine both.
How often should ETL connectors be reviewed and updated?
Review connectors after every source API version release, and at minimum quarterly.
What is the difference between ETL and ELT, and does it affect how connectors are maintained?
ETL transforms data before loading, and ELT loads first and transforms in the destination. Connectors handle extract and load in both patterns, so maintenance practices apply equally.
