Agendor Connector: sync your CRM data to your warehouse without managing pipelines
Sync Agendor data to your data warehouse via Erathos. 15 endpoints (deals, contacts, funnels, products) to BigQuery, Redshift, and more.
Managed connector to sync organizations, people, deals, tasks, users, products, and more from Agendor to BigQuery, Redshift, PostgreSQL, Databricks, and Amazon S3. Create your account and test it now.
Every data team supporting a B2B sales operation eventually hits the same roadblock: sales funnel data lives in Agendor, the rest of the analytical stack lives in the data warehouse, and joining the two becomes an unplanned engineering project. What starts as a Python script running via cron on EC2 turns, three months later, into a pipeline that nobody understands, that fails silently, and that no engineer wants to inherit.
Erathos launches the managed connector for Agendor. Fifteen available endpoints, five supported destinations, zero pipeline code to write or maintain.
The problem with custom-built Agendor pipelines
The Agendor API authenticates via a personal token available directly in the integration panel. The pattern is simple enough to convince any engineer to build an in-house integration in an afternoon. The real cost shows up later, and it is predictable.
Pagination that silently breaks
Deals and people have volumes and pagination behavior that vary depending on the database size and update frequency. Logic that works for a small org won't necessarily work when the contact base grows or during bulk imports. When the API adjusts some default, the pipeline stops bringing in all records without raising an error.
Unannounced rate limits
The Agendor API operates with rate limiting. In a normal incremental sync with medium volumes, you stay below the limit. During a historical backfill, or when someone decides to reprocess a year of deals over the weekend, the pipeline begins receiving errors. If retries are not properly implemented with exponential backoff, entire data windows are lost without warning.
Silent schema evolution
Agendor supports custom fields. Sales teams add fields, rename categories, and create new business types. New fields appear, existing fields become nullable, structures change. The dbt model that ran flawlessly starts failing in production, or worse: it keeps running, but calculating revenue on a field that disappeared. This inconsistency hits the sales team's dashboard before making it to the data monitor.
Nonexistent observability
A 200 OK on the HTTP call doesn't mean the data arrived correctly. Without record counts per endpoint per run, comparison with the previous window, and volume drop alerts, you are flying blind. A pipeline that ran successfully could have fetched zero new deals because the cursor got stuck.
The worst-case scenario isn't the pipeline that breaks and alerts. It's the pipeline that executes successfully and delivers corrupted data, while the funnel conversion metric displayed on the sales manager's dashboard is already calculated from a broken source.
What is possible when Agendor data reaches your warehouse
End-to-end funnel analysis with joined data
With deals and deal_stages in the warehouse, you can calculate stage duration by rep, product, and customer segment with record-level precision—not just as an aggregate exported from the Agendor UI. This model does not exist natively in any Agendor report; it's only possible once the data is in your warehouse alongside other tables in your data model.
Combining sales pipeline with product and financial data
By joining organizations and people with the customer table from your financial system or with product usage events, you can answer questions like: which sectors have the highest close rates? What is the average LTV by lead origin? Which product categories generate deals with the shortest cycle? These questions require Agendor organizations and ERP accounts to live in the same warehouse, joined by a business key.
Rep productivity analysis
With tasks and users in the warehouse, you can build a view of rep activity with daily or weekly granularity. Unlike native Agendor reports, which are pre-aggregated, here you have the raw data to build any time window or dimension the business needs: how many tasks per closed deal, what is the activity ratio among reps, who is above or below average on follow-ups.
Loss reason and stage-by-stage conversion analysis
The loss_reasons and deal_statuses endpoints provide the data that explains why deals are lost and at which stage. With this in the warehouse, you can model conversion rates per stage of the funnel, identify specific bottlenecks in the sales process, and cross-reference loss reasons with deal attributes like value, industry, assigned rep, and lead origin.
Portfolio and product mix analysis
The products and product_categories endpoints bring the product catalog and how they appear in deals. With this in the warehouse, you can analyze which products are most frequently packaged together, which have the highest inclusion rate in won deals, and how product mix varies by customer segment. This information remains locked inside Agendor without a robust data pipeline.
Segmentation and pipeline health analysis
With funnels and categories in the warehouse, you can model the health of each sales funnel separately, which is especially useful for companies running multiple parallel sales processes (outbound, expansion, renewal). Joining deals per funnel with CRM sectors unlocks market penetration analysis by segment that the native Agendor dashboard cannot deliver.
What's available in the connector
The Agendor connector delivers fifteen endpoints ready to be materialized in your destination warehouse:
Endpoint | What it contains |
|---|---|
organizations | Companies registered in the CRM |
people | Contacts and linked individuals |
deals | Sales opportunities and pipeline deals |
tasks | Registered tasks and activities |
users | Agendor account users |
categories | Customer and business categories |
sectors | Customer industries/sectors |
lead_origins | Configured lead sources |
loss_reasons | Reasons for lost deals |
funnels | Configured sales funnels |
deal_stages | Stages of each sales funnel |
deal_statuses | Status of the deals |
metrics | Sales performance metrics |
products | Registered products and services |
product_categories | Product categories |
Supported destinations: BigQuery, Redshift, PostgreSQL, Databricks, and Amazon S3.
How to authenticate
The connector authentication requires a single field:
Token: The personal API token of the Agendor account
To find the token, log into Agendor and navigate to: Menu > Integrações. The token is available directly on that screen.
Why outsource ingestion to Erathos
The premise of the connector is straightforward: maintaining the ingestion pipeline shouldn't be the data team's responsibility. Pagination, rate limits, retry logic with backoff, schema evolution, failure alerts, volume drop alerts, backfills. All of this is handled by whoever operates the ingestion platform.
With the connector configured, the platform delivers:
End-to-end visibility of every execution
Extraction runtime by endpoint, record counts per window, which windows were processed, and where retries occurred. When a conversion metric changes on the dashboard and the sales manager opens a ticket with the data team, you have a complete 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 to the alerting integrations your team already uses, like Slack, Discord, or email. No need to write this code.
Reprocessing as a standard operation
When you need to reprocess a window—either because you modified your dbt models or a source fix was made—it's a platform operation, not an ad-hoc sequence of DELETE + INSERT queries in the warehouse.
Correct pagination, rate limit management, schema evolution, and backfills are the platform's job. The data team focuses on modeling, not the plumbing.
Available pipelines
Agendor → BigQuery: https://www.erathos.com/pipelines/agendor-bigquery
Agendor → Redshift: https://www.erathos.com/pipelines/agendor-redshift
Agendor → PostgreSQL: https://www.erathos.com/pipelines/agendor-postgresql
Agendor → Databricks: https://www.erathos.com/pipelines/agendor-databricks
Agendor → Amazon S3: https://www.erathos.com/pipelines/agendor-amazon-s3
Get started now
Create your Erathos account and connect Agendor to your warehouse in minutes. With the API token, the first records land in your destination with zero pipeline code to write, maintain, or monitor.
Sales data generated daily shouldn't stay locked inside a CRM, disconnected from the rest of your analytical model. Even worse: in a home-grown pipeline that will demand your team's attention every single month.
See the full connector documentation at docs.erathos.com/connectors/apis/agendor.
Managed connector to sync organizations, people, deals, tasks, users, products, and more from Agendor to BigQuery, Redshift, PostgreSQL, Databricks, and Amazon S3. Create your account and test it now.
Every data team supporting a B2B sales operation eventually hits the same roadblock: sales funnel data lives in Agendor, the rest of the analytical stack lives in the data warehouse, and joining the two becomes an unplanned engineering project. What starts as a Python script running via cron on EC2 turns, three months later, into a pipeline that nobody understands, that fails silently, and that no engineer wants to inherit.
Erathos launches the managed connector for Agendor. Fifteen available endpoints, five supported destinations, zero pipeline code to write or maintain.
The problem with custom-built Agendor pipelines
The Agendor API authenticates via a personal token available directly in the integration panel. The pattern is simple enough to convince any engineer to build an in-house integration in an afternoon. The real cost shows up later, and it is predictable.
Pagination that silently breaks
Deals and people have volumes and pagination behavior that vary depending on the database size and update frequency. Logic that works for a small org won't necessarily work when the contact base grows or during bulk imports. When the API adjusts some default, the pipeline stops bringing in all records without raising an error.
Unannounced rate limits
The Agendor API operates with rate limiting. In a normal incremental sync with medium volumes, you stay below the limit. During a historical backfill, or when someone decides to reprocess a year of deals over the weekend, the pipeline begins receiving errors. If retries are not properly implemented with exponential backoff, entire data windows are lost without warning.
Silent schema evolution
Agendor supports custom fields. Sales teams add fields, rename categories, and create new business types. New fields appear, existing fields become nullable, structures change. The dbt model that ran flawlessly starts failing in production, or worse: it keeps running, but calculating revenue on a field that disappeared. This inconsistency hits the sales team's dashboard before making it to the data monitor.
Nonexistent observability
A 200 OK on the HTTP call doesn't mean the data arrived correctly. Without record counts per endpoint per run, comparison with the previous window, and volume drop alerts, you are flying blind. A pipeline that ran successfully could have fetched zero new deals because the cursor got stuck.
The worst-case scenario isn't the pipeline that breaks and alerts. It's the pipeline that executes successfully and delivers corrupted data, while the funnel conversion metric displayed on the sales manager's dashboard is already calculated from a broken source.
What is possible when Agendor data reaches your warehouse
End-to-end funnel analysis with joined data
With deals and deal_stages in the warehouse, you can calculate stage duration by rep, product, and customer segment with record-level precision—not just as an aggregate exported from the Agendor UI. This model does not exist natively in any Agendor report; it's only possible once the data is in your warehouse alongside other tables in your data model.
Combining sales pipeline with product and financial data
By joining organizations and people with the customer table from your financial system or with product usage events, you can answer questions like: which sectors have the highest close rates? What is the average LTV by lead origin? Which product categories generate deals with the shortest cycle? These questions require Agendor organizations and ERP accounts to live in the same warehouse, joined by a business key.
Rep productivity analysis
With tasks and users in the warehouse, you can build a view of rep activity with daily or weekly granularity. Unlike native Agendor reports, which are pre-aggregated, here you have the raw data to build any time window or dimension the business needs: how many tasks per closed deal, what is the activity ratio among reps, who is above or below average on follow-ups.
Loss reason and stage-by-stage conversion analysis
The loss_reasons and deal_statuses endpoints provide the data that explains why deals are lost and at which stage. With this in the warehouse, you can model conversion rates per stage of the funnel, identify specific bottlenecks in the sales process, and cross-reference loss reasons with deal attributes like value, industry, assigned rep, and lead origin.
Portfolio and product mix analysis
The products and product_categories endpoints bring the product catalog and how they appear in deals. With this in the warehouse, you can analyze which products are most frequently packaged together, which have the highest inclusion rate in won deals, and how product mix varies by customer segment. This information remains locked inside Agendor without a robust data pipeline.
Segmentation and pipeline health analysis
With funnels and categories in the warehouse, you can model the health of each sales funnel separately, which is especially useful for companies running multiple parallel sales processes (outbound, expansion, renewal). Joining deals per funnel with CRM sectors unlocks market penetration analysis by segment that the native Agendor dashboard cannot deliver.
What's available in the connector
The Agendor connector delivers fifteen endpoints ready to be materialized in your destination warehouse:
Endpoint | What it contains |
|---|---|
organizations | Companies registered in the CRM |
people | Contacts and linked individuals |
deals | Sales opportunities and pipeline deals |
tasks | Registered tasks and activities |
users | Agendor account users |
categories | Customer and business categories |
sectors | Customer industries/sectors |
lead_origins | Configured lead sources |
loss_reasons | Reasons for lost deals |
funnels | Configured sales funnels |
deal_stages | Stages of each sales funnel |
deal_statuses | Status of the deals |
metrics | Sales performance metrics |
products | Registered products and services |
product_categories | Product categories |
Supported destinations: BigQuery, Redshift, PostgreSQL, Databricks, and Amazon S3.
How to authenticate
The connector authentication requires a single field:
Token: The personal API token of the Agendor account
To find the token, log into Agendor and navigate to: Menu > Integrações. The token is available directly on that screen.
Why outsource ingestion to Erathos
The premise of the connector is straightforward: maintaining the ingestion pipeline shouldn't be the data team's responsibility. Pagination, rate limits, retry logic with backoff, schema evolution, failure alerts, volume drop alerts, backfills. All of this is handled by whoever operates the ingestion platform.
With the connector configured, the platform delivers:
End-to-end visibility of every execution
Extraction runtime by endpoint, record counts per window, which windows were processed, and where retries occurred. When a conversion metric changes on the dashboard and the sales manager opens a ticket with the data team, you have a complete 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 to the alerting integrations your team already uses, like Slack, Discord, or email. No need to write this code.
Reprocessing as a standard operation
When you need to reprocess a window—either because you modified your dbt models or a source fix was made—it's a platform operation, not an ad-hoc sequence of DELETE + INSERT queries in the warehouse.
Correct pagination, rate limit management, schema evolution, and backfills are the platform's job. The data team focuses on modeling, not the plumbing.
Available pipelines
Agendor → BigQuery: https://www.erathos.com/pipelines/agendor-bigquery
Agendor → Redshift: https://www.erathos.com/pipelines/agendor-redshift
Agendor → PostgreSQL: https://www.erathos.com/pipelines/agendor-postgresql
Agendor → Databricks: https://www.erathos.com/pipelines/agendor-databricks
Agendor → Amazon S3: https://www.erathos.com/pipelines/agendor-amazon-s3
Get started now
Create your Erathos account and connect Agendor to your warehouse in minutes. With the API token, the first records land in your destination with zero pipeline code to write, maintain, or monitor.
Sales data generated daily shouldn't stay locked inside a CRM, disconnected from the rest of your analytical model. Even worse: in a home-grown pipeline that will demand your team's attention every single month.
See the full connector documentation at docs.erathos.com/connectors/apis/agendor.