VTex + Redshift

VTex is an e-commerce platform for building and managing online stores with an omnichannel experience focus.

With Erathos, you can integrate VTex data into Redshift in just a few minutes. Our platform handles the entire data movement process to your data warehouse on AWS and lets you join this data with other sources. That way, your time goes toward what really creates value — extracting insights and making data-driven decisions.

What VTex data does Erathos sync with Redshift?

The integration automatically syncs the main VTex objects:

  • Orders — status, amount, line items, and shipping address

  • Products — SKU, price, inventory, and description

  • Customers — profile data, purchase history, and reviews

  • Payments — method, status, and amounts

  • Reviews — ratings, comments, and replies

  • Returns — reason, status, and refunded amount

Why sync VTex with Redshift?

In Redshift, you connect this data to the AWS ecosystem — QuickSight for dashboards, SageMaker for ML, and S3 as staging. Columnar storage optimized for analytical queries at petabyte scale with predictable billing. Ideal for teams already running on AWS.

How it works

Erathos connects to VTex via the official API and syncs your data incrementally — only new or updated records are processed in each run. You choose the sync frequency (from every 5 minutes to daily), which objects to sync, and the Redshift destination. The sync uses incremental processing that handles only new or updated records — keeping Redshift costs predictable. You choose the sync frequency, the objects to sync, and the destination schema. Every run is logged with full observability: runtime, rows processed, and alerts via Slack or email.

No credit card required.

Why do data teams choose Erathos for VTEX?

VTEX data in Redshift in minutes

VTEX data in Redshift in minutes

Ready-to-use VTEX connector

Connect VTex to Redshift and automatically export data. Centralize marketplace data for analysis — no spreadsheets, no scripts.

Full control over your VTex pipelines

Set the schedule, frequency, and sync type at the table level. Set the frequency and sync type per table. Incremental sync for Redshift — only new records are processed in each run, without reprocessing everything from scratch and with predictable AWS costs.

End-to-end observability

No more finding out about VTEX failures only when the business team complains. Every execution is logged with run time, processed rows, and error context. Automatic alerts via Slack, Discord, or email as soon as anything behaves unexpectedly — always fresh data for your analysis.

No credit card required

No credit card required

Why companies move data from VTEX to Redshift with Erathos

Centralizing VTEX data in Redshift has never been easier

Erathos is a data ingestion platform built for operations and data teams. With the VTex connector, you automatically centralize operational data and metrics in Redshift — always fresh data, complete observability across every run, and zero maintenance.

Our Customers

Writing data-driven stories

Writing data-driven stories

"Erathos has revolutionized the way WePayments approaches data management. With its ability to integrate data from multiple SaaS into a single data warehouse, our technical team can now focus more effectively on the company's core business. With Erathos, we’ve been able to implement dashboards that provide insights across all areas of the company. This has not only enriched our organizational culture but also significantly improved our decision-making process."

Matheus Gobato Nunes

CTO & co-founder @WePayments

"Erathos has revolutionized the way WePayments approaches data management. With its ability to integrate data from multiple SaaS into a single data warehouse, our technical team can now focus more effectively on the company's core business. With Erathos, we’ve been able to implement dashboards that provide insights across all areas of the company. This has not only enriched our organizational culture but also significantly improved our decision-making process."

Matheus Gobato Nunes

CTO & co-founder @WePayments

Trusted by data-driven companies

Simplified data ingestion

Move your data in minutes

Move your data in minutes

1

Select your data source

More than 80 plug-and-play connectors to consolidate data from multiple sources, eliminate time-consuming manual processes, and create a streamlined path forward.

2

Setup your pipeline

Manage your pipeline seemlessly. Select a sync hour, frequency and type at a table/endpoint level.

3

Select your data warehouse

Choose between Amazon S3, BigQuery, Databricks, Redshift and PosgreSQL to centrlize your data

FAQ

Frequently Asked Questions

Frequently Asked Questions

What is Erathos, and how can it help my business?

Erathos is a data ingestion platform built for reliability, transparency, and control. We help data teams connect tools like VTex to their data warehouse — with full visibility into every run, zero maintenance, and none of the black-box behavior of traditional market tools.

What VTEX data does Erathos sync to Redshift?

Erathos connects VTex to your Data Warehouse, syncing orders, products, customers, inventory, reviews, and sales performance data incrementally and automatically.

How often does Erathos sync data from VTEX to Redshift?

You can configure sync frequency from every 5 minutes up to daily, at the table level. Erathos uses incremental sync—only new or updated records are processed on each run, keeping the VTex pipeline efficient and Redshift costs predictable.

What happens if a VTEX sync fails?

Erathos automatically detects failures and sends alerts to your email, Slack, or Discord with full context — not just “job failed.” Smart retries handle transient errors, and every run is logged with runtime, rows processed, and error context so your team can debug in minutes, not hours.

Is there a free trial period for the VTEX connector?

Yes. Every Erathos connector includes a 14-day free trial. Connect VTex to Redshift and start syncing right away — no credit card required.

Data ingestion with control, observability, and scale

Data ingestion with control, observability, and scale