How does Kondado replicate Tray data to Amazon S3?

Kondado uses a direct integration to extract data from your Tray account and load it into your specified S3 bucket. You authenticate both services through the platform interface, select which of the 14 available pipelines you want to replicate, and define your update schedule. The service handles the data extraction automatically without requiring any custom scripts or manual file exports.

What Tray eCommerce data can I store in Amazon S3?

You can replicate 14 different pipelines including Customers, Orders, Payments, Products, Discount Coupons, and Newsletters to your S3 bucket. Each pipeline contains specific fields such as order details, transaction values, customer emails, and product attributes. This gives you comprehensive coverage of your eCommerce operations from sales transactions to inventory management.

How often does Tray data update in my S3 bucket?

Kondado updates your S3 data on a configurable schedule that you control, with options ranging from every 5 minutes to daily intervals. You select the frequency when setting up each pipeline based on your analytics needs and data freshness requirements. This automated scheduling ensures your data lake stays current without manual intervention.

What file format does Tray data use in Amazon S3?

The data is stored in structured formats optimized for analytics engines like Athena, Presto, and Dremio that commonly query S3 data lakes. This format supports complex SQL queries and joins across multiple data sources. You can easily connect this data to BigQuery or other analytics platforms for visualization and reporting.

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

Yes, you can replicate data from multiple sources into the same S3 bucket to create a unified data lake architecture. Kondado supports connections to 80+ data sources including marketing platforms, CRMs, and advertising tools. This allows you to join Tray eCommerce data with customer acquisition metrics and create comprehensive analytics workflows.

Which Tray pipelines include customer information for S3 storage?

The Customers pipeline contains core contact information including names, emails, and registration dates, while the Newsletters pipeline tracks subscription status and engagement history. The Orders and Payments pipelines also include customer identifiers that enable you to link transactions to specific accounts. This data supports customer segmentation and retention analysis in your analytics tools.

How do I query Tray data once it's in Amazon S3?

You can query your Tray data using standard SQL through engines like Athena, Presto, or Dremio that connect directly to S3 storage. Many business intelligence tools including Power BI and Looker Studio can connect to S3 to visualize your eCommerce metrics. This setup enables complex analysis across historical order data and customer behavior patterns without impacting your live Tray system. ### HowTo

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Tray to Amazon S3: Automated Data Replication

Sending Tray data to Amazon S3 is straightforward with Kondado’s direct integration. Connect your Tray account as a data source and select Amazon S3 as your destination to begin replicating eCommerce data automatically. You can choose from 14 available pipelines including Customers, Orders, and Payments, then configure your preferred update frequency from every 5 minutes to daily intervals. The process requires no coding: simply authenticate your accounts, select the specific pipelines you need, and let Kondado handle the extraction and loading.

Kondado replicates 14 Tray pipelines containing 328 fields to Amazon S3 automatically on a configurable schedule ranging from every 5 minutes to daily, enabling analysts to query eCommerce data with Athena, Presto, or Dremio directly from S3 storage.

Once your Tray data lands in Amazon S3, you unlock powerful analytics capabilities by combining it with other business data sources. Store historical order information, customer records, and product catalogs in your scalable data lake to support complex SQL queries and business intelligence workflows. This setup allows you to build custom reports in Power BI or Looker Studio while maintaining the flexibility to use any analytics tool that connects to S3.

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Available Tray Pipelines for Amazon S3

With the Orders and Order Details pipelines, you can analyze purchase patterns and delivery performance by querying structured data directly in Amazon S3 using standard SQL. The Customers pipeline enables segmentation analysis and lifetime value calculations when combined with transaction history, while the Products and Sold Products pipelines provide inventory insights and sales velocity metrics. This data supports advanced analytics workflows where you can join Tray eCommerce information with marketing data from other platforms to create comprehensive business intelligence in your data lake, or visualize results in Power BI and BigQuery.

FAQ

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Tray data available for Amazon S3

available pipelines

328

extractable fields

Available integrations

Integration	Description
Customers	Includes information such as name, email, and registration date, along with modification date and last visit.
Discount Coupons	Contains details such as coupon code, creation date, and discount value, along with usage restrictions.
Newsletters	Presents information about the customer's email, newsletter status, and creation and modification dates.
Payments	Includes data on payment methods, status, and values of transactions made by customers.
Orders	Provides information such as order ID, order date, and delivery status, along with customer details.
Order Details	Includes detailed information such as order ID, billing address, and additional cart values.
Order Details: Mercado Livre	Presents data such as customer ID, estimated delivery date, and information about the purchased product.
Additional Product Information for Orders	Contains additional information such as product ID and product name related to each order.
Order Invoices	Includes data such as invoice ID and product CFOP, essential for accounting.
Order Transactions	Provides information on transaction status and values associated with each order made.
Products	Table contains information about products, including id, name, and variants. Fields such as attributes_id and attributes_value provide additional details.
Products (Kits)	Table presents product kits, detailing id and attributes. Fields such as attributes_customer_attribute_id provide information about the characteristics of the kits.
Sold Products	Table displays sold products, including id and sales information. Fields such as order_id and order_item_quantity help understand sales performance.
Products: Variants	Table details product variants, including id and attributes. Fields such as additionalproductinfo_product_id and additionalproductinfo_variant_id provide specific information about each variant.

Customers

Includes information such as name, email, and registration date, along with modification date and last visit.

Discount Coupons

Contains details such as coupon code, creation date, and discount value, along with usage restrictions.

Newsletters

Presents information about the customer's email, newsletter status, and creation and modification dates.

Payments

Includes data on payment methods, status, and values of transactions made by customers.

Orders

Provides information such as order ID, order date, and delivery status, along with customer details.

Order Details

Includes detailed information such as order ID, billing address, and additional cart values.

Order Details: Mercado Livre

Presents data such as customer ID, estimated delivery date, and information about the purchased product.

Additional Product Information for Orders

Contains additional information such as product ID and product name related to each order.

Order Invoices

Includes data such as invoice ID and product CFOP, essential for accounting.

Order Transactions

Provides information on transaction status and values associated with each order made.

Products

Table contains information about products, including id, name, and variants. Fields such as attributes_id and attributes_value provide additional details.

Products (Kits)

Table presents product kits, detailing id and attributes. Fields such as attributes_customer_attribute_id provide information about the characteristics of the kits.

Sold Products

Table displays sold products, including id and sales information. Fields such as order_id and order_item_quantity help understand sales performance.

Products: Variants

Table details product variants, including id and attributes. Fields such as additionalproductinfo_product_id and additionalproductinfo_variant_id provide specific information about each variant.

See all available data →

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How to send Tray data to Amazon S3

Sync data automatically — no code, no manual exports.

Connect Tray to Kondado

Navigate to the data sources section and add Tray as a new data source by entering your API credentials. This establishes the connection to your eCommerce platform and allows Kondado to access your store data.

Configure Amazon S3 Destination

Set up Amazon S3 as your destination by providing your bucket name and AWS region details. This creates the target location where your Tray pipelines will be stored for analysis with Athena or other query engines.

Select Pipelines and Schedule

Choose which of the 14 Tray pipelines you want to replicate, such as Orders or Customers, and set your update frequency from every 5 minutes to daily. This configures the automated data flow that keeps your S3 data lake current.

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