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 funnel 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 launches the managed connector for Piperun. Nine endpoints available, 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 a quick integration in an afternoon. The real cost comes later, and it is predictable.
Inconsistent pagination across endpoints
Deals, persons, and activities have very different volumes and update frequencies. The pagination logic that works fine for one endpoint won't necessarily cover another, and when deal volume grows, the pipeline might stop bringing in complete records without throwing any visible errors.
Custom fields changing without warning
Sales teams change custom fields in deals and pipelines frequently, usually without warning whoever maintains the integration. The dbt model that used to run smoothly starts breaking in production, or worse: it keeps running, but missing a field without anyone noticing.
Stages and pipelines changing structure
Revenue teams reorganize funnel stages and create new sales pipelines as part of normal 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 finds out about 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, and the forecast metric that goes to the board is calculated on a broken baseline.
What's possible when 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 in each stage, and pipeline velocity by sales rep or team, with single-record precision, rather than using aggregates exported from a native dashboard.
Blending sales 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 a higher post-sales churn rate? This is only possible when companies and deals live alongside the other tables in your analytical model.
Sales productivity analysis
With activities and persons, you can build a view of call volume, meetings, and tasks per rep, blending it with deals closed during the same period. Unlike Piperun's native reports, the raw data here allows for any time window or breakdown your business needs.
Funnel structure change auditing
With pipelines, stages, and custom_fields in the warehouse, you can track how the commercial structure evolved over time, which is key to 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 blend qualitative deal context with quantitative funnel metrics—data that is usually trapped inside the CRM's UI.
What's 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 | Negotiations and sales opportunities |
activities | Calls, meetings, and tasks from the sales team |
pipelines | Sales funnels configured in the account |
stages | Steps within 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: pipeline maintenance engineering shouldn't be the data team's responsibility. Pagination, schema drifts on custom fields, shifting stages, failure alerts, drop-in-volume alerts, backfills—all of that is the responsibility of the ingestion platform provider.
With the connector configured, the platform delivers:
End-to-end visibility of every run
Extraction time per endpoint, record count per window, which windows were processed, and where retries occurred. When the number of closed deals shifts in a dashboard and someone from sales asks why, you have a full audit trail to find the root cause.
Out-of-the-box alerting
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.
Backfills as a first-class operation
When you need to reprocess a window because your modeling changed or because a data fix arrived at the source, this is a platform operation, not an improvised series of DELETE + INSERT queries in the warehouse.
Correct pagination, schema evolution management, and backfills are the platform's job. The data and analytics 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 your API token, the first data points load into your destination with zero pipeline code to write, maintain, or monitor.
Sales data generated daily shouldn't stay locked inside a SaaS, disconnected from the rest of your analytical model. Or worse: in a home-grown pipeline that will steal your team's focus every single month.
Check out the full 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 same wall: the commercial funnel 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 launches the managed connector for Piperun. Nine endpoints available, 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 a quick integration in an afternoon. The real cost comes later, and it is predictable.
Inconsistent pagination across endpoints
Deals, persons, and activities have very different volumes and update frequencies. The pagination logic that works fine for one endpoint won't necessarily cover another, and when deal volume grows, the pipeline might stop bringing in complete records without throwing any visible errors.
Custom fields changing without warning
Sales teams change custom fields in deals and pipelines frequently, usually without warning whoever maintains the integration. The dbt model that used to run smoothly starts breaking in production, or worse: it keeps running, but missing a field without anyone noticing.
Stages and pipelines changing structure
Revenue teams reorganize funnel stages and create new sales pipelines as part of normal 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 finds out about 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, and the forecast metric that goes to the board is calculated on a broken baseline.
What's possible when 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 in each stage, and pipeline velocity by sales rep or team, with single-record precision, rather than using aggregates exported from a native dashboard.
Blending sales 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 a higher post-sales churn rate? This is only possible when companies and deals live alongside the other tables in your analytical model.
Sales productivity analysis
With activities and persons, you can build a view of call volume, meetings, and tasks per rep, blending it with deals closed during the same period. Unlike Piperun's native reports, the raw data here allows for any time window or breakdown your business needs.
Funnel structure change auditing
With pipelines, stages, and custom_fields in the warehouse, you can track how the commercial structure evolved over time, which is key to 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 blend qualitative deal context with quantitative funnel metrics—data that is usually trapped inside the CRM's UI.
What's 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 | Negotiations and sales opportunities |
activities | Calls, meetings, and tasks from the sales team |
pipelines | Sales funnels configured in the account |
stages | Steps within 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: pipeline maintenance engineering shouldn't be the data team's responsibility. Pagination, schema drifts on custom fields, shifting stages, failure alerts, drop-in-volume alerts, backfills—all of that is the responsibility of the ingestion platform provider.
With the connector configured, the platform delivers:
End-to-end visibility of every run
Extraction time per endpoint, record count per window, which windows were processed, and where retries occurred. When the number of closed deals shifts in a dashboard and someone from sales asks why, you have a full audit trail to find the root cause.
Out-of-the-box alerting
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.
Backfills as a first-class operation
When you need to reprocess a window because your modeling changed or because a data fix arrived at the source, this is a platform operation, not an improvised series of DELETE + INSERT queries in the warehouse.
Correct pagination, schema evolution management, and backfills are the platform's job. The data and analytics 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 your API token, the first data points load into your destination with zero pipeline code to write, maintain, or monitor.
Sales data generated daily shouldn't stay locked inside a SaaS, disconnected from the rest of your analytical model. Or worse: in a home-grown pipeline that will steal your team's focus every single month.
Check out the full connector documentation at docs.erathos.com/connectors/apis/piperun.