Sync Freshdesk Data
to Flat Files
& Spreadsheets
Automatically sync your Freshdesk data to cloud storage and spreadsheets with Flatly's no-code, triggerless, turnkey integration service.
About the integration: Freshdesk is a customer support software also classified as a ticketing software or a helpdesk that allows companies to effectively manage their customer care and support function. Flatly integrates with Freshdesk's official APIs.
Available Datasets
Flatly can sync the following datasets from Freshdesk.
- Agents
- Business Hours
- Companies
- Company Fields
- Contacts
- Contact Fields
- Forums+Categories Combo
- Forums+Topics Combo
- Email Configs
- Groups
- Helpdesk Settings
- Products
- Roles
- SLA Policies
- Solutions+Categories Combo
- Solutions+Folders Combo
- Solutions+Articles Combo
- Surveys
- SatisfactionRatings-Alltime
- SatisfactionRatings-Last X days Date-ranged
- Tickets-Alltime
- Tickets-Last X days Date-ranged
- Tickets+Conversations-Alltime Combo
- Tickets+Conversations-Last X days Combo Date-ranged
- Tickets+SatisfactionRatings-Alltime Combo
- Tickets+SatisfactionRatings-Last X days Combo Date-ranged
- Tickets+TimeEntries-Alltime Combo
- Tickets+TimeEntries-Last X days Combo Date-ranged
- Tickets-Search by Query
- Tickets-Search by Query + Conversations-Brief Combo
- Tickets-Search by Query + Conversations-Full Combo
- Tickets-Search by Dates
- Tickets-Search by Dates + Conversations-Brief Combo
- Tickets-Search by Dates + Conversations-Full Combo
- Ticket Fields
- Time Entries-All
Send Freshdesk data to
Flatly can sync Freshdesk data to any of these destinations.
- Airtable Airtable
- Amazon S3 XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- Azure Storage XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- Box XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- CSV
- Cloud Storage
- Dropbox XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- Excel
- Excel Online XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- FTP XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- Google BigQuery NDJSON
- Google Cloud Storage XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- Google Drive Google Sheets (Parsed) Google Sheets (Raw) XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- Google Looker Studio
- Google Sheets Google Sheets (Parsed) Google Sheets (Raw) XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- Looker NDJSON
- Markdown
- Microsoft Power BI
- OneDrive XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- OneDrive for Business XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- SFTP XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- SharePoint XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- Smartsheet Smartsheet
- Zoho Sheet Zoho Sheet
Note: Some destinations are aliased, for all intents and purposes they are functionally equivalent.
Querying Freshdesk - AI or ETL?
Many SaaS applications and databases can be directly queried within AI chat apps using connectors. For ad-hoc, single-user requests, this direct architecture is superior—it offers real-time visibility, high customizability, and bypasses the need for complex data pipelines.
However, ETL (Extract, Transform, Load) can be more favorable when certain requirements exist. You might consider transitioning to an ETL system and a centralized data store when:
- Collaboration is required: Multiple stakeholders (colleagues, partners) need consistent access to the same shared single source of truth.
- Downstream audiences: The queried data must be routed into other internal IT systems, published to team dashboards, or preserved in official records rather than remaining isolated in a chat window.
- Data is complex: The raw data needs heavy cleaning or joining before an AI or non-technical user can accurately understand it.
- Internal capabilities are limited: Your organization lacks the in-house programming resources or infrastructure necessary to reliably build, deploy, and maintain custom agentic workflows or complex data integrations from scratch.
- Numerous accounts: When a high volume of identities or accounts within a data source need to be analyzed in bulk, a sophisticated connection manager is needed.
- Concurrency & infrastructure: If many data sets need to be analyzed in parallel, concurrency can become an issue. The device hosting an AI agent can become saturated without concurrency controls in place, whereas an ETL pipeline is built to handle parallel workloads reliably.