Piperun Connector: Your B2B sales data without any data pipeline maintenance
Managed connector to sync deals, contacts, companies, and activities from Piperun to BigQuery, Redshift, Databricks, and more with Erathos.
Managed connector to sync companies, persons, deals, activities, pipelines, stages, notes, files, and custom_fields from Piperun to BigQuery, Redshift, PostgreSQL, Databricks, Supabase, Azure SQL Server, and Amazon S3. Create your account and test it now.
Every sales team running on Piperun eventually hits the same wall: the commercial pipeline lives in the CRM, the rest of the analytical stack lives in the warehouse, and connecting the two turns into an engineering project that nobody planned for. What starts as a manual CSV export every Monday ends, a few months later, in a script that nobody wants to touch.
Erathos is launching the managed connector for Piperun. Nine available endpoints, seven supported destinations, zero pipeline code to write or maintain.
The problem with custom-built Piperun pipelines
The Piperun API is authenticated via a personal token generated inside the account itself. The pattern is simple enough for any engineer to build their own integration in an afternoon. The real cost shows up later, and it is highly predictable.
Inconsistent pagination across endpoints
Deals, persons, and activities have very different volumes and update frequencies. A pagination logic that works well for one endpoint won't necessarily cover another, and when deal volume grows, the pipeline can stop fetching complete records without throwing any visible error.
Custom fields changing without notice
Sales teams change custom fields in deals and pipelines frequently, usually without warning the team maintaining the integration. The dbt model that used to run smoothly starts breaking in production, or worse: it keeps running but with one less field, without anyone noticing.
Stages and pipelines changing structure
Commercial teams reorganize funnel stages and create new sales pipelines as a normal part of business operations. A custom-built integration that assumes a fixed stage structure breaks silently as soon as the sales team reorganizes the funnel.
Non-existent observability
A 200 OK API response doesn't mean the right data arrived. Without record counts per endpoint, comparison with the previous run, and drop-in-volume alerts, the team only discovers the issue when the sales pipeline report is already wrong.
The worst-case scenario isn't the pipeline that breaks and triggers an alert. It's the pipeline that runs successfully and delivers incomplete data, meaning the forecast metric going up to the board is already calculated on top of corrupted data.
What you can do once Piperun data lands in the warehouse
Sales forecasting with real granularity
With deals and stages in the warehouse, you can calculate conversion rates per funnel stage, average time spent in each stage, and pipeline velocity by sales rep or team, with record-level precision, not as an aggregate exported from a native dashboard.
Combine commercial data with product and financial data
By combining Piperun companies and deals with product usage or billing data already living in the warehouse, you can answer questions like: do accounts that close faster have a different usage profile? Which segments have the highest post-sale churn rate? This is only possible when companies and deals live alongside the other tables of your analytical model.
Sales productivity analysis
With activities and persons, you can build a view of call volume, meetings, and tasks per sales rep, crossing this with closed deals in the same period. Unlike Piperun's native reporting, here the raw data allows for any time window or breakdown your business needs.
Funnel change auditing
With pipelines, stages, and custom_fields in the warehouse, you can track how the commercial structure evolved over time, useful for understanding if a drop in conversion is a real sales issue or just a change in how the funnel is modeled.
Interaction history per deal
Notes and files bring the textual history and attachments associated with each deal. With this in the warehouse, you can cross qualitative deal context with quantitative funnel metrics, something that is typically locked inside the CRM UI.
What is available in the connector
The Piperun connector delivers nine endpoints ready to be materialized in your destination warehouse:
Endpoint | What it contains |
|---|---|
companies | Companies registered in the CRM |
persons | Contacts and people associated with companies |
deals | Deals and sales opportunities |
activities | Calls, meetings, and tasks from the sales team |
pipelines | Sales funnels configured in the account |
stages | Stages of each sales funnel |
notes | Notes associated with deals and contacts |
files | Files and attachments linked to CRM records |
custom_fields | Custom fields configured by the team |
Supported destinations: BigQuery, Redshift, PostgreSQL, Databricks, Supabase, Azure SQL Server, and Amazon S3.
How to authenticate
Connector authentication requires a single field:
- Token: the personal API token of the Piperun account
To find the token, log into Piperun, click on your profile icon in the top right corner, and navigate to Settings > Integrations > API. Your personal token will be listed there.
Full documentation: https://docs.erathos.com/connectors/apis/piperun
Why outsource ingestion to Erathos
The premise of the connector is straightforward: managing ingestion engineering shouldn't be the data team's responsibility. Pagination, changing custom fields, reorganized stages, failure alerts, volume drop alerts, backfills, all of this is the responsibility of the ingestion platform operator.
With the connector configured, the platform delivers:
End-to-end visibility of every run
Extraction time per endpoint, record counts per window, which windows were processed, and where retries occurred. When the number of closed deals changes in the dashboard and someone from sales asks why, you have a complete audit trail to find the root cause.
Out-of-the-box alerts
Execution failures, volume drops per endpoint, and window delays are detected and routed through the alerting integrations your team already uses. No need to write this code yourself.
Reprocessing as a supported operation
When you need to reprocess a window, either because the modeling changed or because a data correction arrived from the source, it is a platform operation, not an improvised sequence of DELETE + INSERT in the warehouse.
Correct pagination, schema evolution management, and backfills are the platform's job. The sales and data teams focus on the models, not the plumbing.
Available pipelines
- Piperun → BigQuery: https://www.erathos.com/pipelines/piperun-bigquery
- Piperun → Redshift: https://www.erathos.com/pipelines/piperun-redshift
- Piperun → PostgreSQL: https://www.erathos.com/pipelines/piperun-postgresql
- Piperun → Databricks: https://www.erathos.com/pipelines/piperun-databricks
- Piperun → Supabase: https://www.erathos.com/pipelines/piperun-supabase
- Piperun → Azure SQL Server: https://www.erathos.com/pipelines/piperun-azure-sql-server
- Piperun → Amazon S3: https://www.erathos.com/pipelines/piperun-amazon-s3
Get started now
Create your Erathos account and connect Piperun to your warehouse in minutes. With the API token, the first data points arrive at the destination without any pipeline code to write, maintain, or monitor.
Sales data generated daily shouldn't be locked inside a SaaS, disconnected from the rest of your analytical model. Or worse: in a home-grown pipeline that will cost your team attention every single month, forever.
Check the full connector documentation at docs.erathos.com/connectors/apis/piperun.