FreshdeskPostgreSQL

Freshdesk + PostgreSQL

Freshdesk is a cloud-based customer support platform that offers a variety of tools to help companies manage and automate their support processes. Postgres, on the other hand, is a highly robust and scalable open-source relational database management system. It supports a wide range of data types and advanced features.

With Erathos, your Freshdesk data lands in PostgreSQL as a reliable operational source, ready to power internal applications or reporting using standard SQL, without manual sync scripts.

Get started

No credit card required. Upgrade whenever you want.

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.

What Freshdesk data does Erathos sync to PostgreSQL?

The integration automatically syncs key Freshdesk objects:

  • Tickets status, priority, assignee, and resolution time
  • Interactions messages, replies, and conversation history
  • Agents individual performance and support workload
  • SLAs compliance, breaches, and metrics by category
  • CSAT satisfaction ratings and customer feedback
  • Tags and categories taxonomy of contact reasons

Why sync Freshdesk to PostgreSQL?

In PostgreSQL, your Freshdesk data is available as a reliable operational source, ready to power internal applications or analytics using standard SQL.

How it works

Erathos connects to Freshdesk via its official API and syncs your data incrementally: only new or updated records are processed on each run, keeping pipelines fast and PostgreSQL costs predictable. You choose the sync frequency (ranging from 5 minutes to daily), the objects to sync, and the target dataset. Every run is logged with full observability: execution time, rows processed, errors with context, and instant alerts via Slack or email if anything goes wrong.

Why companies move data from Freshdesk to PostgreSQL with Erathos

Consolidating Freshdesk data in PostgreSQL has never been easier.

Erathos is a data ingestion platform for support and data teams. With the Freshdesk connector, you automatically centralize tickets, interactions, SLAs, and customer support data in PostgreSQL — support data always available for analytics and dashboards.

Why do data teams choose Erathos for Freshdesk?

Data in your data warehouse in minutes

No credit card required. Upgrade whenever you want.
Our partners

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
Explore

Other paths for your data

Other sources that land in PostgreSQL

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 Freshdesk to their data warehouse—with full observability into every pipeline run, zero maintenance, and none of the opacity of traditional market tools.

Erathos syncs Freshdesk Tickets, Interactions, Agents, SLAs, Tags, and Customer Satisfaction Ratings (CSAT) to PostgreSQL. Custom ticket fields and support categories are also automatically exported.

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

Data ingestion with control, observability and scale