Connector as a Service
Any API to your warehouse.
SourceQ designs, builds and keeps your custom API connectors running: authentication, pagination, retries, schema changes, monitoring and the maintenance nobody has time for. Your data team works on the data instead of moving it.
- Fixed-price connector delivery
- Monitoring and maintenance included
- Deployed in your cloud or ours — with code you own
When vendor connectors and low-code are not enough
Your data team should create value — not maintain plumbing.
Building the first API call is easy. Production connectors require pagination, checkpointing, token renewal, rate-limit handling, retries, schema-change detection, monitoring and ongoing maintenance.
SourceQ takes that work on — the build, and the years of keeping it running.
We handle
Connector engineering, deployment, monitoring, failures, maintenance and source-system changes.
Your team handles
Data modelling, analytics, AI use cases and the decisions that move the business forward.
Ways we can help
Three ways in, one standard on the other side
Whether the connector does not exist yet, already runs, or has run for too long — it ends the same way: documented, monitored and owned by you.
New connector
A source system with no vendor connector — or one that does not do what you need. We design, build and deliver it at a fixed price, three data models included.
What we build →Support an existing connector
A connector that already runs in production. Where possible, we take it on — the monitoring, the failures and the source-system changes, with guaranteed hours. Optional, and never required to keep it running.
How support works →Rewrite legacy connectors
The scripts, cron jobs and loaders that work until they don't. We rebuild them as standard connectors — checkpointed, monitored, documented — without interrupting the data they deliver.
How a rebuild runs →
A worked example
Choose the data you need. We work out the endpoints behind it.
You ask for Employees. That dataset happens to need four API resources — which is our problem to solve, not four things to pay for.
You get the model and the raw data behind it
Every resource we read to build a data model is also landed in your warehouse in its raw form. Nothing is hidden and nothing is discarded — so a figure can be traced to the payload it came from, a dispute is settled against rows rather than against our word, and a second data model over the same resources needs no re-extraction.
You don't pay per endpoint
One data model may use one API resource or six. The price comes from the type of dataset — how its resources relate, how hard it is to keep in sync, how much transformation it needs — never from how many calls it took to assemble.
Inside every connector
The two-thirds of the work that nobody has time for
A working API client is an afternoon's work. Everything below is what separates it from something you can put in production and forget. It is handled once per connector, identically every time, and shared by every data model running on it.
- Authentication
- Token refresh, expiry and re-auth mid-run
- Pagination
- Traversal to exhaustion, with a check for silent caps
- Rate limiting
- Adaptive backoff, bounded concurrency
- Incremental sync
- Watermark, cursor or client-side change detection
- Schema handling
- New fields land; type changes alert instead of corrupting
- Checkpointing
- An interrupted run resumes rather than restarts
- Retries
- Declared policy, dead-letter for unprocessable records
- Idempotency
- Re-running is always safe
- Backfill
- A separate, re-runnable job — executed in full, not sampled
- Monitoring
- Freshness, row-count anomalies, alerts with runbooks
- Documentation
- A runbook naming the real failure modes
Ownership
At handover, it is yours
No subscription. No SourceQ runtime. No licence check, no phone-home. If we ceased to exist tomorrow your connector would keep running, and anyone could maintain it — with the raw data already in your warehouse to rebuild from.
- The connector as a Docker image — it runs anywhere
- The data models you asked for, in your warehouse
- The raw API resources behind them, landed unmodified
- Source code, in your repository
- The infrastructure definitions, in your cloud account
- The credentials, in your secret manager
- The data — always landed in your warehouse, whoever runs the connector
- The monitors and alert routes
- The runbook and documentation
Connectors
Any source, to your warehouse
REST, GraphQL, SOAP, private and internal APIs — into BigQuery, Snowflake or Databricks.
REST API → BigQuery
A documented REST API landing in BigQuery on a schedule, with incremental sync and a tested backfill.
Base Connector · Standard source build
3 data models included. Then: Employees, Customers, Orders +1
REST API → Snowflake
A REST API landing in Snowflake through a staged load, with incremental sync and a tested backfill.
Base Connector · Standard source build
3 data models included. Then: Employees, Absences, Products
REST API → Databricks
A REST API landing in Databricks via object storage and Auto Loader, as Delta tables you own.
Base Connector · Standard source build
3 data models included. Then: Employees, Events, Customers
Why SourceQ
Four things our competitors do not do
You own it
The data models, the raw data behind them, the source code, the infrastructure definitions and the documentation — in your accounts from the first commit. No subscription, no runtime, nothing to stay on. Revoke our access and everything keeps running.
A price before the work
Name the datasets you need; each one is classified into one of four published types and priced the same day, read from the API's own documentation. You are never charged per endpoint or per table.
Production, not a working script
Token refresh, adaptive backoff, checkpointed resume, schema-drift handling, a backfill that has actually been run, and alerts that name the cause. That is two-thirds of the work and the part that never gets prioritised.
We will point you elsewhere
If Fivetran or Airbyte already has a connector for your API, that is the better answer and we say so in writing. It costs us the deal and it is why you can believe the next recommendation.
How we work
Nine steps. The first four are free.
You get an architecture, a fixed price and a delivery window before you commit to anything. There is exactly one meeting.
01
Sign up and request a connector
Free. Set up your organisation and repository, then tell us which business datasets you want — Employees, Absences, Orders — in your own words.
02
Assessment
The connector is profiled once. Then each dataset you asked for is classified into Simple, Standard, Advanced or Custom.
03
Estimate
Two subtotals in four-hour blocks: what the environment costs, and what the connector costs. Then one row per data model.
04
Confirm scope
One call. Which data models, cadence, backfill depth, acceptance criteria, order.
05
Discovery
We call your API for real, prove the incremental cursor moves, size the backfill, and confirm the price in writing.
06
Build
The connector once, then each data model on top of it.
07
Test
Four checks: schema conformance, completeness, idempotent replay, recovery.
08
Deploy
Into your cloud account, through your pipeline, from your repository.
09
Handover
One hour, recorded. Then our access can be revoked and nothing stops.
Insights
Notes on APIs that do not behave
incremental
EXAMPLE: Incremental sync when the API has no updated_at
Placeholder article on client-side change detection, and why an API with no incremental capability is the most expensive property to discover late.
pagination
EXAMPLE: The API said 10,000 records. There were 41,000.
Placeholder article on pagination that silently caps, and how to detect it before it becomes a data quality incident.
Tell us which data you need
Name the datasets — Employees, Absences, Orders — and send the API documentation link. We work out which resources each one needs, classify it, and send back a fixed price and a delivery window. Free, usually the same day. If a vendor connector would serve you better, we will say that instead.
Get an estimate