TinyDatabricks

Tiny + Databricks

Tiny ERP is a lightweight enterprise resource planning (ERP) system for small and mid-sized businesses that centralizes processes such as sales, finance, and inventory.

With Erathos, you can integrate data from Tiny into Databricks in just a few minutes. Our platform handles the entire data movement process into your lakehouse platform and makes it possible to join this data with other sources. That frees up your time for what really creates value — extracting insights and making data-driven decisions.

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Trusted by great companies

Ace Startups
Kamino
Convenia
Vakinha
Linus
Rabbot
Auvo
Roqt
Xperiun
Tecnova
WePayments
Brick
CCM
Simplified extraction and ingestion

Move data in a few clicks

1

Connect your source

Authenticate your account and you're done. 100+ ready-made connectors, no code required.

2

Configure the sync

Pick the tables, the schedule and the update type: batch, cursor-based incremental or CDC.

3

Choose your destination

Load into BigQuery, Redshift, Databricks, PostgreSQL, ClickHouse, Amazon S3 and more.

Which Tiny data does Erathos sync with Databricks?

The integration automatically syncs Tiny's core objects:

  • Orders and sales — order number, status, items, and amounts
  • Customers — profile data, purchase history, and balance
  • Products and inventory — code, available quantity, and movements
  • Finance — accounts payable, accounts receivable, journal entries, and balances
  • Suppliers — profile data, purchase orders, and commercial terms
  • Invoice — issuance, status, and tax information

Why sync Tiny with Databricks?

In Databricks, you can 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 Tiny through its official API and syncs data incrementally — only new or updated records are processed on each run. You choose the sync frequency (from every 5 minutes to daily), which objects to sync, and the destination in Databricks. Syncs use Delta Lake writes with configurable partitioning. You choose the sync frequency, the objects to sync, and the target schema. Each run is logged with full observability: execution time, rows processed, and alerts via Slack or email.

Why companies move data from Tiny to Databricks with Erathos

Centralizing data from Tiny in Databricks has never been easier

Erathos is a data ingestion platform built for operations and data teams. With the Tiny connector, you automatically centralize operational data and metrics in Databricks — always up-to-date data, with full observability into every run and zero maintenance.

Why do data teams choose Erathos for Tiny?

Tiny data in Databricks in minutes

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Building data-driven stories

Every new source implementation, if I had to do it myself, would take about two or three months. With Erathos, it's done in a few hours. I don't need the skills of a data engineer to generate real value from the company's data.

Bruno Rosso
Bruno RossoData Analyst, Brick Software

Erathos revolutionized data management at WE. By integrating multiple SaaS tools into a single DW, our technical team now focuses on the core business. We implemented dashboards with insights across every area, enriching our organizational culture and improving our decision-making.

Matheus Nunes
Matheus NunesCTO & co-founder, WePayments

We used to have a lot of rework, and now we don't. It's more efficient to work this way. If it's not your core business, hire Erathos. Spend your time modeling your own domain, not someone else's.

Carlos Schwabe
Carlos SchwabeCo-Founder & CDO, Brick

If I didn't have this infrastructure, I'd need three or four people to do what Erathos does today. I was able to generate real value from my data with a much leaner setup than I thought I'd need. That's the core of building a data-driven culture: the team only uses data when they can trust it.

Thiago Paz
Thiago PazCFO, Brick

Erathos brought a practical turnaround at CCM. We were able to integrate financial systems, CRM, and processes with BigQuery in just a few clicks, with no technical team required. That gave us a reliable data warehouse that powers automations, dashboards, and even our customer service bots.

Gustavo Loforte
Gustavo LoforteTechnology Manager, CCM Group

The robustness and efficiency of Erathos's connectors — whether for Meta, Google, RD Station, or ERPs like Bling and Conta Azul — make the whole process much faster and more reliable. The technical documentation is extremely well put together and intuitive, which makes implementation much easier.

Sayuri Valente
Sayuri ValenteProject Manager, Xperiun
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Other paths for your data

Other sources that land in Databricks

CRMs, ERPs, ad platforms and databases, all through the same managed pipeline.

See every source
FAQ

Frequently Asked Questions

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

Erathos connects Tiny’s core modules to your data warehouse: finance, inventory, orders, customers, suppliers, and more. Data is extracted incrementally, without impacting operations.

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

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.

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

Data ingestion with control, observability and scale