Data Pipelines
Get your data flowing where it needs to be
Integrations and pipelines that move data between the tools you run, reliably and on schedule, instead of someone exporting a CSV every week.
Built from Data Analytics and API Integrations
Built for your specific tools
Connectors written for the systems you use, not a generic one-size adapter. If someone on your team moves data between these by hand today, that is the pipeline.
- Store and payments
- Shopify, WooCommerce, Razorpay, Stripe
- CRM and sales
- HubSpot, Zoho CRM, Salesforce
- Accounting
- Tally, Zoho Books, QuickBooks
- Your product's database
- Postgres, MySQL, MongoDB
- Ads and analytics
- Google Ads, Meta Ads, GA4
- Files and sheets
- Google Sheets, CSV on SFTP, email attachments
- Support and ops
- Freshdesk, Zendesk, Shiprocket
- Where it lands
- BigQuery, Snowflake, Postgres, or another tool
Examples, not a limit - anything with an API, an export or a file.
Checked before anyone reads it
Deduplication, transformation, and validation, so what lands is trustworthy. A check that fails holds the load, so a dashboard shows yesterday's right number rather than today's wrong one.
| Check | On | Rule | Last run |
|---|---|---|---|
| Row count | stg_orders | Within 0.5% of Shopify's own count | Pass |
| Nulls | orders_clean.customer_id | None allowed | Pass |
| Duplicates | stg_payments.payment_id | Each one appears once | Pass |
| Freshness | stg_payments | Newest row under 2 hours old | FailLoad held, #data told |
| Allowed values | orders_clean.currency | INR or USD | Pass |
| Matches | rev_daily | Every payment ties to an order | Pass |
An example run - the checks are written for your tables.
What happens at two in the morning
Scheduled or event-driven pipelines with alerts when something actually fails. One nightly run, start to finish:
- 01:55
Credentials checked
Each source is pinged first, so an expired token is caught before the run.
- 02:00
Extract
Only new and changed rows are read from each source.
- 02:01
A source says no
Razorpay returns 503. It is retried after 30 seconds and succeeds. Nobody is woken.
- 02:02
Clean and check
Deduplicated, joined, and the checks above are run.
- 02:03
Load
Written to the warehouse in one step - never a half-loaded table.
- 08:00
Morning
Dashboards read last night's data. If a step had failed three times, the alert would already be waiting.
On a schedule, or on an event
Nightly or hourly for most sources; a webhook or a new file for the ones that can't wait until morning.
An alert means something failed
Not a digest of green ticks. A message goes out only after the retries are spent, and it names the step.
Questions we get asked
What happens when a source is down?
The step is retried with a wait between attempts. Only a failure that keeps happening sends an alert, and it names the step that failed.
Scheduled, or as things happen?
Either. A nightly or hourly schedule for most sources, and an event - a webhook, a new file - for the ones that can't wait.
How do we know the data that lands is right?
Checks run before every load - row counts, nulls, duplicates, freshness - and a failed check holds the load instead of publishing bad data.
Do you use generic connectors?
Connectors are written for the systems you actually use, so the awkward parts of each - its limits, its date formats, its duplicates - are handled.
We only need to move data once. Is this for us?
Probably not. A single one-time migration is a smaller job than a pipeline, and we would scope it that way.
What if a system has no API?
An export or a file drop works. If a system has no API and no export at all, there is nothing for a pipeline to read.
Someone still exporting a CSV every week?
Tell us the systems and what should move between them - we come back with the run we would build, and the checks it would carry.