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Sync Harvest Data to Flat Files & Spreadsheets

Harvest

Automatically sync your Harvest data to cloud storage and spreadsheets with Flatly's no-code, triggerless, turnkey integration service.

Historical & Dynamic Data
One-way Sync
For Non-technical Users

About the integration: Harvest makes time tracking painless, so you get the timesheet data you need to keep up with your team, analyze your projects, and invoice clients. Flatly integrates with Harvest's official APIs.

Connect Harvest Which Harvest API does Flatly use?

Available Datasets

Flatly can sync the following datasets from Harvest.

  • Contacts-All
  • Contacts-Last X days Date-ranged
  • Clients-All
  • Clients-Last X days Date-ranged
  • Estimates-Alltime
  • Estimates-Last X days Date-ranged
  • Expenses-Alltime
  • Expenses-Last X days Date-ranged
  • Expenses-Billed Alltime
  • Expenses-Billed Last X days Date-ranged
  • Invoices-Alltime
  • Invoices-Last X days Date-ranged
  • Invoices+LineItems-Alltime Combo
  • Invoices+LineItems-Last X days Combo Date-ranged
  • Invoices+Messages-Alltime Combo
  • Invoices+Messages-Last X days Combo Date-ranged
  • Invoices+Payments-Alltime Combo
  • Invoices+Payments-Last X days Combo Date-ranged
  • Invoice ItemCategories
  • Projects-All Alltime
  • Projects-All Last X days Date-ranged
  • Projects-Active Alltime
  • Projects-Active Last X days Date-ranged
  • Projects-All-Alltime+TimeEntries Combo
  • Projects-All-Last X days+TimeEntries Combo Date-ranged
  • Projects-Active-Alltime+TimeEntries Combo
  • Projects-Active-Last X days+TimeEntries Combo Date-ranged
  • Roles
  • Report-ExpensesClients Last X days Date-ranged
  • Report-TimeClients Last365days
  • Report-TimeClients Last X days Date-ranged
  • Report-TimeProjects Last X days Date-ranged
  • Report-TimeTasks Last X days Date-ranged
  • Report-TimeTeam Last X days Date-ranged
  • Report-Uninvoiced Last X days Date-ranged
  • Tasks-All Alltime
  • Tasks-All Last X days Date-ranged
  • Tasks-Active Alltime
  • Tasks-Active Last X days Date-ranged
  • TaskAssignmentsAll Alltime
  • TaskAssignmentsAll Last X days Date-ranged
  • TaskAssignmentsActive Alltime
  • TaskAssignmentsActive Last X days Date-ranged
  • TaskAssignmentsAll Alltime by Project
  • TaskAssignmentsAll Last X days by Project Date-ranged
  • TaskAssignmentsActive Alltime by Project
  • TaskAssignmentsActive Last X days by Project Date-ranged
  • TimeEntries-Alltime
  • TimeEntries-Last X days Date-ranged
  • TimeEntries-Billed Alltime
  • TimeEntries-Billed Last X days Date-ranged
  • TimeEntries-Running Alltime
  • TimeEntries-Running Last X days Date-ranged
  • TimeEntries-Unbilled Alltime
  • TimeEntries-Unbilled Last X days Date-ranged
  • UsersAll+BillableRates Combo
  • UsersActive+BillableRates Combo
  • UsersAll+CostRates Combo
  • UsersActive+CostRates Combo
  • UserAssignmentsAll Alltime
  • UserAssignmentsAll Last X days Date-ranged
  • UserAssignmentsActive Alltime
  • UserAssignmentsActive Last X days Date-ranged
  • UserAssignmentsAll Alltime by Project
  • UserAssignmentsAll Last X days by Project Date-ranged
  • UserAssignmentsActive Alltime by Project
  • UserAssignmentsActive Last X days by Project Date-ranged

Send Harvest data to

Flatly can sync Harvest data to any of these destinations.

Note: Some destinations are aliased, for all intents and purposes they are functionally equivalent.

Querying Harvest - 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.