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 exact same wall: the sales funnel lives in the CRM, the rest of the analytics stack lives in the data 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, as a script that nobody wants to touch.
Erathos launches the managed connector for Piperun. Nine endpoints available, seven supported destinations, zero pipeline code to write or maintain.
The problem with building custom Piperun pipelines
The Piperun API is authenticated via a personal token generated directly within the user account. The standard is simple enough for any engineer to build a quick integration in an afternoon. The real cost comes 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 great for one endpoint won't necessarily cover another, and as deal volume grows, the pipeline might stop fetching complete records without raising any visible errors.
Custom fields changing without notice
Sales teams frequently modify custom fields for deals and pipelines, usually without warning the owners of the integration. The dbt model that used to run smoothly starts breaking in production, or worse: it keeps running, but with one missing field without anyone noticing.
Stages and pipelines shifting structure
Go-to-market teams reorganize pipeline stages and create new sales funnels as a normal part of business operations. A custom-built integration that assumes a hardcoded stage structure breaks silently the second the sales team reorganizes the funnel.
Non-existent observability
A 200 OK API response doesn't mean the right data actually made it. Without record counts per endpoint, comparison with the previous run, and alerts on volume drops, the data team only discovers there's an issue when the sales pipeline report is already wrong.
The worst case scenario isn't the pipeline that breaks and fires an alert. It's the pipeline that runs successfully but delivers incomplete data, meaning the forecast metric presented to the board is calculated on top of corrupted data.
What you can build once Piperun data lands in your warehouse
Sales forecasting with actual granularity
With deals and stages in the warehouse, you can calculate conversion rates per stage of the funnel, average time in each stage, and pipeline velocity by sales rep or team—with record-level accuracy, not as an aggregate exported from a native dashboard.
Join sales data with product and financial data
By merging companies and deals from Piperun with product usage or billing data already living in the data warehouse, you can answer questions like: do accounts that close faster have a different product usage profile? Which segments have the highest post-sales churn rate? This is only possible when companies and deals live right next to the other tables in your analytics model.
Sales productivity analysis
With activities and persons, you can build a complete view of call volumes, meetings, and tasks per rep, joining them with closed deals in the same period. Unlike the native Piperun reporting, here the raw data allows for any custom timeframe or breakdown your business needs.
Audit trail for funnel changes
With pipelines, stages, and custom_fields in the warehouse, you can track how the sales structure evolved over time, which is helpful to understand if a dip in conversion is an actual sales problem or just a change in how the funnel is modeled.
Interaction history per deal
Notes and files bring the conversation history and attachments associated with each deal. With this in the warehouse, you can join qualitative negotiating context with the 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 individuals 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 in to Piperun, click on your profile icon in the top right corner, and go 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 connector's premise is direct: maintaining ingestion pipelines shouldn't be the data team's responsibility. Pagination, changing custom fields, shifting stages, error alerts, volume drop alerts, and backfills—all of this is handled by the ingestion platform.
Once the connector is configured, the platform provides:
End-to-end visibility of every execution
Extraction times per endpoint, record counts per window, which windows were processed, and where retries occurred. When closed deal metrics change 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 delay alerts are automatically detected and routed through the alerting integrations your team already uses. No need to write this code yourself.
Supported historical reloads
When you need to reprocess a window because your data model changed or source data was corrected, this is a native platform feature, not an ad-hoc sequence of DELETE + INSERT queries in your warehouse.
Correct pagination, schema evolution handling, and historical backfills are the platform's job. The data and business teams focus on modeling, not 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
Create your Erathos account and connect Piperun to your warehouse in minutes. With just your API token, the first records will land in your 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. Even worse: trapped in a home-grown pipeline that will forever demand your team's support.
Check out the complete connector documentation at docs.erathos.com/connectors/apis/piperun.
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 exact same wall: the sales funnel lives in the CRM, the rest of the analytics stack lives in the data 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, as a script that nobody wants to touch.
Erathos launches the managed connector for Piperun. Nine endpoints available, seven supported destinations, zero pipeline code to write or maintain.
The problem with building custom Piperun pipelines
The Piperun API is authenticated via a personal token generated directly within the user account. The standard is simple enough for any engineer to build a quick integration in an afternoon. The real cost comes 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 great for one endpoint won't necessarily cover another, and as deal volume grows, the pipeline might stop fetching complete records without raising any visible errors.
Custom fields changing without notice
Sales teams frequently modify custom fields for deals and pipelines, usually without warning the owners of the integration. The dbt model that used to run smoothly starts breaking in production, or worse: it keeps running, but with one missing field without anyone noticing.
Stages and pipelines shifting structure
Go-to-market teams reorganize pipeline stages and create new sales funnels as a normal part of business operations. A custom-built integration that assumes a hardcoded stage structure breaks silently the second the sales team reorganizes the funnel.
Non-existent observability
A 200 OK API response doesn't mean the right data actually made it. Without record counts per endpoint, comparison with the previous run, and alerts on volume drops, the data team only discovers there's an issue when the sales pipeline report is already wrong.
The worst case scenario isn't the pipeline that breaks and fires an alert. It's the pipeline that runs successfully but delivers incomplete data, meaning the forecast metric presented to the board is calculated on top of corrupted data.
What you can build once Piperun data lands in your warehouse
Sales forecasting with actual granularity
With deals and stages in the warehouse, you can calculate conversion rates per stage of the funnel, average time in each stage, and pipeline velocity by sales rep or team—with record-level accuracy, not as an aggregate exported from a native dashboard.
Join sales data with product and financial data
By merging companies and deals from Piperun with product usage or billing data already living in the data warehouse, you can answer questions like: do accounts that close faster have a different product usage profile? Which segments have the highest post-sales churn rate? This is only possible when companies and deals live right next to the other tables in your analytics model.
Sales productivity analysis
With activities and persons, you can build a complete view of call volumes, meetings, and tasks per rep, joining them with closed deals in the same period. Unlike the native Piperun reporting, here the raw data allows for any custom timeframe or breakdown your business needs.
Audit trail for funnel changes
With pipelines, stages, and custom_fields in the warehouse, you can track how the sales structure evolved over time, which is helpful to understand if a dip in conversion is an actual sales problem or just a change in how the funnel is modeled.
Interaction history per deal
Notes and files bring the conversation history and attachments associated with each deal. With this in the warehouse, you can join qualitative negotiating context with the 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 individuals 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 in to Piperun, click on your profile icon in the top right corner, and go 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 connector's premise is direct: maintaining ingestion pipelines shouldn't be the data team's responsibility. Pagination, changing custom fields, shifting stages, error alerts, volume drop alerts, and backfills—all of this is handled by the ingestion platform.
Once the connector is configured, the platform provides:
End-to-end visibility of every execution
Extraction times per endpoint, record counts per window, which windows were processed, and where retries occurred. When closed deal metrics change 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 delay alerts are automatically detected and routed through the alerting integrations your team already uses. No need to write this code yourself.
Supported historical reloads
When you need to reprocess a window because your data model changed or source data was corrected, this is a native platform feature, not an ad-hoc sequence of DELETE + INSERT queries in your warehouse.
Correct pagination, schema evolution handling, and historical backfills are the platform's job. The data and business teams focus on modeling, not 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
Create your Erathos account and connect Piperun to your warehouse in minutes. With just your API token, the first records will land in your 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. Even worse: trapped in a home-grown pipeline that will forever demand your team's support.
Check out the complete connector documentation at docs.erathos.com/connectors/apis/piperun.