CalendlyDatabricks
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Calendly + Databricks

Calendly is an online meeting scheduling tool that makes it easy to organize appointments. Databricks, in turn, is a serverless big data solution that provides robust capabilities for storing and analyzing data at scale.

With Erathos, you can integrate Calendly data into Databricks in just a few minutes. Our platform handles the entire data movement process into your analytics environment and makes it possible to combine that data with other sources in your data warehouse. That way, your time is focused on what really drives value—extracting actionable insights and making more data-driven decisions.

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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 Calendly data does Erathos sync with Databricks?

The integration automatically syncs Calendly's core objects:

  • Projects — status, owners, dates, and settings
  • Tasks and subtasks — priority, status, estimates, and owners
  • Sprints — dates, velocity, and planning data
  • Members — users, roles, and workload
  • Comments — discussion and decision history
  • Custom fields — team-defined properties and tags

Why sync Calendly with Databricks?

Calendly reports show project progress, but not the business impact. In Databricks, you connect productivity data with product, revenue, and customer satisfaction metrics—calculating lead time, identifying systemic bottlenecks, and correlating execution speed with business outcomes.

How it works

Erathos connects to Calendly via the official API and syncs your data incrementally—only new or updated records are processed on each run, keeping pipelines fast and Databricks costs predictable. You choose the sync frequency (from every 5 minutes to daily), which objects to sync, and the destination dataset. Each run is logged with full observability: execution time, rows processed, errors with context, and instant alerts via Slack or email if something goes wrong.

Why Companies Move Data from Calendly to Databricks with Erathos

Centralizing Calendly data in Databricks has never been easier

Erathos is a data ingestion platform for operations and data teams. With the Calendly connector, you can automatically export tasks, projects, sprints, and productivity data to Databricks — centralized productivity data ready for analysis.

Why do data teams choose Erathos for Calendly?

Data in your data warehouse 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.

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FAQ

Frequently Asked Questions

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

Erathos syncs Projects, Tasks, Subtasks, Members, Sprints, Statuses, and Comments from Calendly to Databricks. Custom fields, tags, and dependencies between tasks created by the team are also exported.

You can configure the sync frequency from every 5 minutes up to daily, at the table level. Erathos uses incremental synchronization—only new or updated records are processed in each run, keeping the Calendly 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 execution is logged with run time, processed rows, and error context so your team can debug in minutes, not hours.

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

Data ingestion with control, observability and scale