How does Hotmart to Amazon S3 replication work with Kondado?

Kondado connects to your Hotmart account using your API credentials to extract selected data endpoints and load them into your designated S3 buckets. The platform handles data transformation and file organization automatically, delivering structured datasets that are immediately queryable through services like Athena or Presto without requiring manual file management.

What Hotmart data can I replicate to Amazon S3?

You can replicate 11 distinct pipelines covering Subscribers, Sales History, Subscription Transactions, Sales Commissions, and Hotmart Club metrics including student progress and module completion. This includes over 202 fields of data ranging from transaction details and pricing information to student engagement metrics and product catalogs.

How often does Kondado update Hotmart data in my S3 bucket?

Kondado replicates data on a configurable schedule that you control, with options ranging from every 5 minutes to daily updates depending on your analytics requirements. This automated approach ensures your S3 data lake reflects current business conditions without requiring manual intervention or scheduled script maintenance.

What file format does Hotmart data arrive in when sent to Amazon S3?

Data arrives in optimized file formats suitable for analytical querying, typically structured as compressed files that work efficiently with Athena, Presto, and Dremio. This format supports fast data virtualization and integrates seamlessly with business intelligence tools like Power BI or BigQuery when you need to visualize your Hotmart metrics.

Can I combine Hotmart data with other sources in my Amazon S3 data lake?

Yes, you can consolidate Hotmart data alongside information from advertising channels, CRM systems, or additional e-commerce platforms within the same S3 environment. This unified storage approach enables comprehensive cross-platform analytics, allowing you to correlate sales data with marketing spend or support interactions for complete business intelligence in tools like Power BI or Looker Studio .

Do I need coding skills to set up Hotmart to Amazon S3 pipelines?

No coding is required to configure the connection, as Kondado provides a no-code interface for selecting your Hotmart data source and S3 destination. You simply authenticate your accounts, choose which pipelines to replicate, and define your update schedule through the visual dashboard without writing API scripts or managing infrastructure.

Which analytics tools work with Hotmart data stored in Amazon S3?

Hotmart data stored in Amazon S3 integrates directly with visualization platforms including Power BI , Looker Studio , and BigQuery , as well as SQL-based warehouses like PostgreSQL . This flexibility allows you to build custom reports on digital product performance using your preferred business intelligence stack.

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Send Hotmart Data to Amazon S3 Automatically

To send Hotmart data to Amazon S3, you need a data replication platform that connects directly to your Hotmart account and delivers structured files to your S3 buckets. Kondado provides a direct integration that extracts your sales, subscription, and product information from Hotmart and loads it into Amazon S3 on a configurable schedule. This eliminates manual exports and ensures your data lake contains consistent, analysis-ready information for your business intelligence workflows.

Kondado replicates Hotmart data to Amazon S3 through automated pipelines that deliver 11 distinct data endpoints including Subscribers, Sales History, and Hotmart Club metrics directly to your S3 storage on schedules ranging from every 5 minutes to daily, enabling seamless data lake architecture without coding requirements.

Once your Hotmart data lands in Amazon S3, you can query it using Athena, join it with other business data, or feed it into visualization tools like Power BI and Looker Studio. This setup supports complex analytics on digital product performance, subscription trends, and student engagement metrics while maintaining complete control over your storage infrastructure.

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Available Hotmart Pipelines for Amazon S3

The available pipelines include Subscribers, Sales Commissions, and Hotmart Club: Student Progress, giving you comprehensive visibility into both revenue streams and educational content consumption. With this data in Amazon S3, you can build custom dashboards tracking subscription retention rates, analyze commission structures across different products, or monitor student completion percentages to optimize your course offerings. You can also combine Hotmart data with marketing or support sources in your data lake to create unified reports on customer lifetime value, product profitability, and learner engagement patterns that drive strategic decisions for your digital business.

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Hotmart data available for Amazon S3

available pipelines

202

extractable fields

Available integrations

Integration	Description
Subscribers	Records of subscriptions include subscriber_code, subscriber_name, and status, allowing detailed analysis of active and canceled subscriptions.
Sales Commissions	Commission details include transaction, commission_value, and user_name, providing a clear view of commissions generated by each transaction.
Subscriber Purchases	Data on purchases includes subscriber_code, product_id, and price_value, allowing tracking of acquisitions made by subscribers.
Sales Price Breakdown	Detailed pricing information includes product_id, price_value, and price_currency_code, facilitating analysis of sales prices.
Sales History	Sales records include purchase_transaction, product_id, and request_date, allowing analysis of transaction history.
Hotmart Club: Students	Information about students includes subscriber_name, product_id, and status, allowing tracking of students' progress in the club.
Hotmart Club: Modules	Data on modules includes product_id, module_name, and completion_status, allowing analysis of students' progress in each module.
Hotmart Club: Student Progress	Progress records include subscriber_code, module_id, and completion_percentage, facilitating monitoring of students' performance.
Hotmart Club: Pages	Information about pages includes page_id, access_count, and last_access_date, allowing analysis of student engagement.
Products	Product data includes product_id, product_name, and price_value, allowing detailed analysis of the available product portfolio.
Subscription Transactions	Records of subscription transactions, including fields such as transaction code, product ID, and commission value.

Subscribers

Records of subscriptions include subscriber_code, subscriber_name, and status, allowing detailed analysis of active and canceled subscriptions.

Sales Commissions

Commission details include transaction, commission_value, and user_name, providing a clear view of commissions generated by each transaction.

Subscriber Purchases

Data on purchases includes subscriber_code, product_id, and price_value, allowing tracking of acquisitions made by subscribers.

Sales Price Breakdown

Detailed pricing information includes product_id, price_value, and price_currency_code, facilitating analysis of sales prices.

Sales History

Sales records include purchase_transaction, product_id, and request_date, allowing analysis of transaction history.

Hotmart Club: Students

Information about students includes subscriber_name, product_id, and status, allowing tracking of students' progress in the club.

Hotmart Club: Modules

Data on modules includes product_id, module_name, and completion_status, allowing analysis of students' progress in each module.

Hotmart Club: Student Progress

Progress records include subscriber_code, module_id, and completion_percentage, facilitating monitoring of students' performance.

Hotmart Club: Pages

Information about pages includes page_id, access_count, and last_access_date, allowing analysis of student engagement.

Products

Product data includes product_id, product_name, and price_value, allowing detailed analysis of the available product portfolio.

Subscription Transactions

Records of subscription transactions, including fields such as transaction code, product ID, and commission value.

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How to send Hotmart data to Amazon S3

Sync data automatically — no code, no manual exports.

Connect Your Hotmart Account

Authenticate your Hotmart data source in Kondado by providing your API credentials, allowing the platform to access your sales and subscription information for replication to Amazon S3, BigQuery, or PostgreSQL.

Configure Amazon S3 Destination

Enter your AWS S3 bucket details to establish the storage location where your Hotmart pipelines will be delivered as optimized files ready for querying. From Amazon S3, you can easily transfer data to BigQuery or PostgreSQL for additional processing.

Select Pipelines and Schedule Updates

Choose which of the 11 available pipelines to replicate, such as Subscribers or Sales Commissions, and configure your update schedule ranging from 5-minute intervals to daily batches. Once in Amazon S3, visualize your data in Power BI or Looker Studio to track your digital product performance.

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