Sympla + Amazon S3

Sympla is an event ticketing and sales platform for in-person and online events.

With Erathos, you can integrate Sympla data into Amazon S3 in just a few minutes. Our platform handles the entire data movement process to your data lake and makes it possible to join that data with other sources. That way, your time goes where it really creates value — extracting insights and making data-driven decisions.

Which Sympla data does Erathos sync to Amazon S3?

The integration automatically syncs Sympla's main objects:

  • Orders — status, amount, 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 Sympla with Amazon S3?

In Amazon S3 with Iceberg, your data is stored as Parquet files with support for time travel and schema evolution — ready to be queried with Athena, Spark, Trino, or any query engine in your stack. Ideal for low-cost archiving and ML feature stores.

How it works

Erathos connects to Sympla through the official API and syncs your data incrementally — only new or updated records are processed on each run. You choose the sync frequency (from every 5 minutes to daily), the objects to sync, and the destination in Amazon S3. The sync uses automatic partitioning and Parquet file optimization. You choose the sync frequency, the objects to sync, and the target bucket. Each run is logged with full observability: execution time, processed rows, context-rich errors, and alerts via Slack or email.

No credit card required.

Why do data teams choose Erathos for Sympla?

Sympla data in Amazon S3 in minutes

Sympla data in Amazon S3 in minutes

Sympla connector ready to use

Connect Sympla to Amazon S3 and automatically export data. Centralize marketplace data for analysis — no spreadsheets, no scripts.

Full control over your Sympla pipelines

Configure the schedule, frequency, and sync type at the table level. Configure partitioning, file format, and write frequency at the table level. The Iceberg format ensures ACID compliance and schema evolution — without full bucket rewrites.

End-to-end observability

Stop finding out about Sympla failures only when the business team complains. Every run is logged with runtime, processed rows, and error context. Get automated alerts via Slack, Discord, or email as soon as something goes off track — so your data stays fresh for analysis.

No credit card required

No credit card required

Why Companies Move Data from Sympla to Amazon S3 with Erathos

Centralizing data from Sympla in Amazon S3 has never been easier

Erathos is a data ingestion platform built for operations and data teams. With the Sympla connector, you automatically centralize operational data and metrics in Amazon S3 — always up-to-date data, full observability for 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 Sympla to their data warehouse — with full observability into every run, zero maintenance, and none of the black-box behavior of traditional market tools.

What Sympla data does Erathos sync to Amazon S3?

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

How often does Erathos sync data from Sympla to Amazon S3?

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 Sympla pipeline efficient and Amazon S3 costs predictable.

What happens if a Sympla 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 Sympla connector?

Yes. Every Erathos connector includes a 14-day free trial. Connect Sympla to Amazon S3 and start syncing right away — no credit card required.

Data ingestion with control, observability, and scale

Data ingestion with control, observability, and scale