No-code pipeline · Pagar.me → Amazon S3

Send data from Pagar.me to Amazon S3

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From Pagar.me to Amazon S3: managed, scheduled, no code.

Kondado provides a direct integration between Pagar.me and Amazon S3, replicating 8 pipelines including Customers, Charges, Orders, and Receivables on a configurable schedule. The platform delivers 344 fields of payment and financial data directly to your S3 storage, enabling you to query transaction records with Athena, Presto, or Dremio without manual API extraction.

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Send Pagar.me Data to Amazon S3 Automatically

Setting up automated data flows from Pagar.me to Amazon S3 takes just minutes with Kondado’s no-code platform. You simply authenticate your Pagar.me account, configure your Amazon S3 bucket as the destination, and select which pipelines to replicate. Kondado handles the extraction and loading on your chosen schedule, whether every 5 minutes or daily, ensuring your S3 bucket always contains fresh payment data for analysis.

Once your data lands in Amazon S3, you can combine Pagar.me transaction records with other business data sources to build comprehensive financial reports. The automated replication eliminates manual CSV exports and ensures your data lake contains consistent, up-to-date payment information for business intelligence workflows.

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Available Pipelines for Analysis

The Customers pipeline brings comprehensive buyer profiles including contact details and addresses into your data lake, enabling you to segment payment behavior by geography or demographics using SQL queries in Athena. When combined with the Charges pipeline, which captures transaction amounts, dates, and status history, you can analyze revenue patterns and identify successful payment methods across your e-commerce operations. Finance teams leverage the Receivables pipeline to track pending and completed payouts with due dates and amounts, creating automated cash flow forecasting models that update as new settlement data arrives in your S3 bucket.

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Replicated to Amazon S3

Pagar.me data available for Amazon S3

Tables Kondado writes into your Amazon S3, on a schedule you control.

available pipelines

344

extractable fields

Amazon S3

Destination

Available integrations

Customers

Includes information such as customer name, email, and status, along with address and phone data like area code and number.

Customers: Cards

Records card details including brand, expiration date, and status, along with cardholder name and card digits.

Customers: Addresses

Contains address data such as city, state, and country, along with creation date and address status.

Charges

Includes information on charges made, such as amount, date, and status, along with details of the associated customer.

Balance Operations

Records operations related to balance, including operation type, amount, and transaction date.

Orders

Contains details of orders placed, such as total amount, status, and creation date, along with customer information.

Recipients

Includes information about recipients, such as name, document, and status, along with creation and update data.

Receivables

Contains information about receivables, including amount, due date, and status, along with details of the associated customer.

See all available data → Send Pagar.me to Amazon S3 →

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How to send Pagar.me data to Amazon S3

Sync data automatically — no code, no manual exports.

Connect Your Pagar.me Account

Authenticate your Pagar.me data source in Kondado by providing your API credentials, allowing the platform to access your payment transactions and customer records.

Configure Amazon S3 Destination

Enter your S3 bucket name and AWS region in the destination settings, specifying the folder path where Kondado should store your replicated Pagar.me datasets.

Select Pipelines and Schedule

Choose from the 8 available pipelines such as Charges or Receivables, then set your preferred update frequency from every 5 minutes to daily to maintain current data in Amazon S3.

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Send data from Pagar.me to other destinations

Pagar.me > Looker Studio

Pagar.me > Power BI

Other visualizations

Pagar.me > Amazon S3

Pagar.me > BigQuery

Pagar.me > Excel

Pagar.me > Google Sheets

Pagar.me > MySQL

Pagar.me > PostgreSQL

Pagar.me > Redshift

Pagar.me > SQL Server

Choose a tool to visualize your Pagar.me data

If the software you need is not listed, drop us a messagem. You can use almost every tool

Pagar.me > Airtable

Pagar.me > Alteryx

Pagar.me > Amazon Quicksight

Pagar.me > BIMachine

Pagar.me > Cortex

Pagar.me > Data Studio

Pagar.me > Geckoboard

Pagar.me > Grafana

Pagar.me > IBM Cognos Analytics

Pagar.me > Klipfolio

Pagar.me > Looker Studio

Pagar.me > Looker

Pagar.me > Looqbox

Pagar.me > Metabase

Pagar.me > Microstrategy

Pagar.me > Pentaho

Pagar.me > Power BI

Pagar.me > Qlik Cloud Analytics

Pagar.me > Qlik Sense

Pagar.me > Qlik

Pagar.me > QlikView

Pagar.me > Redash

Pagar.me > Superset

Pagar.me > Tableau

Pagar.me > BI TOTVS

Pagar.me > Zoho Analytics

Frequently Asked Questions (FAQ)

Answers about sending Pagar.me data to Amazon S3 automatically

How do I automate Pagar.me data exports to Amazon S3? ▼

Kondado connects directly to your Pagar.me account using secure authentication, then automatically extracts your selected pipelines and loads them into your designated S3 bucket. You configure the replication schedule during setup, choosing intervals from every 5 minutes to daily based on your business requirements. The process requires no manual CSV downloads or custom scripts, maintaining a continuous flow of payment data into your storage environment.

What Pagar.me data can I replicate to Amazon S3? ▼

Kondado offers 8 distinct pipelines covering Customers, Cards, Addresses, Charges, Balance Operations, Orders, Recipients, and Receivables, totaling 344 available fields. You can select specific pipelines such as Charges for transaction history or Receivables for settlement tracking, or replicate all available data endpoints simultaneously. Each pipeline contains normalized financial data including amounts, dates, status fields, and relational identifiers connecting customers to their transactions.

How often does Kondado update Pagar.me data in S3? ▼

You control the replication frequency through Kondado's scheduling interface, with options ranging from every 5 minutes for near-real-time analysis to hourly or daily batches for cost optimization. The platform checks for new and updated records at each scheduled interval, appending or updating files in your S3 bucket to reflect the latest payment activity. This configurable approach lets you balance data freshness with storage and processing costs based on your specific analytics needs.

What file format does Pagar.me data use in Amazon S3? ▼

Kondado delivers Pagar.me data to S3 in structured formats optimized for analytics engines, typically Parquet or JSON Lines depending on your configuration, enabling efficient querying with BigQuery, Athena, or Presto. The files are organized in partitioned directory structures based on extraction dates, making it simple to run time-series analysis on transaction trends. This format compatibility ensures your existing data stack can immediately begin processing payment records without conversion overhead.

Can I combine Pagar.me data with other sources in S3? ▼

Yes, Kondado enables you to replicate data from multiple sources into the same S3 bucket or data lake, allowing you to join Pagar.me transaction records with information from Google Sheets, PostgreSQL, or other platforms. By storing diverse datasets in a centralized S3 location, you can create unified reports that correlate payment data with marketing campaigns, inventory levels, or customer support interactions. This consolidation eliminates data silos and provides a complete view of your business operations.

Do I need coding skills to send Pagar.me data to S3? ▼

No coding is required to configure the Pagar.me to S3 pipeline, as Kondado provides a visual interface where you authenticate accounts and select data endpoints through point-and-click navigation. The platform handles API pagination, data type mapping, and file formatting automatically, removing the complexity of building custom ETL workflows. Business analysts and finance teams can manage the entire replication process independently without engineering support.

Which analytics tools work with Pagar.me data stored in S3? ▼

Once your Pagar.me data resides in Amazon S3, you can query it directly using Athena, Presto, or Dremio, or load it into Power BI, Looker Studio, and other business intelligence platforms that support S3 connections. The standardized file formats ensure compatibility with popular visualization tools, enabling you to build custom dashboards tracking payment conversion rates, revenue trends, and settlement schedules. You can also connect the data to BigQuery for advanced SQL analysis alongside other marketing or sales datasets.

Try out all the features for free for 14 days

Related on Kondado

Try out all the features for free for 14 days