Sympla + Databricks

Sympla is a solution for managing and selling tickets for in-person and online events.

With Erathos, you can integrate Sympla data into Databricks in just a few minutes. Our platform handles the entire data movement process to your lakehouse platform and lets you join that data with other sources. That way, your time is focused on what truly creates value — uncovering insights and making data-driven decisions.

What Sympla data does Erathos sync with Databricks?

The integration automatically syncs Sympla's main objects:

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

  • Products — SKU, price, inventory, and description

  • Customers — records, purchase history, and reviews

  • Payments — method, status, and amounts

  • Reviews — ratings, comments, and replies

  • Returns — reason, status, and refunded amount

Why sync Sympla with Databricks?

In Databricks, you combine this data with Delta Lake for ACID transactions, time travel, and schema evolution. Data and ML teams get unified access through Python, SQL, and Spark — ideal for feature engineering, predictive models, and large-scale analytics.

How it works

Erathos connects to Sympla through its 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), the objects to sync, and the destination in Databricks. Sync writes to Delta Lake with configurable partitioning. You choose the sync frequency, the objects to sync, and the target schema. Each 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 Sympla?

Sympla data in Databricks in minutes

Sympla data in Databricks in minutes

Sympla connector ready to use

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

Full control over your Sympla pipelines

Configure schedule, frequency, and sync type at the table level. Configure frequency, sync type, and partitioning per table. Data lands in Databricks in Delta Lake—ready for ML, analytics, and ad hoc queries with predictable cost.

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 Databricks with Erathos

Centralizing Sympla data in Databricks 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 Databricks — always up to date, with full observability into 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 Databricks?

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 Databricks?

You can configure sync frequency at the table level, from every 5 minutes to once a day. Erathos uses incremental sync—only new or updated records are processed on each run, keeping the Sympla pipeline efficient and Databricks 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 Databricks and start syncing right away — no credit card required.

Data ingestion with control, observability, and scale

Data ingestion with control, observability, and scale