How does Toggl to PostgreSQL replication work with Kondado?

Kondado uses a direct integration to extract data from your Toggl account and load it into your PostgreSQL database on a configurable schedule. You authenticate your Toggl credentials, select which pipelines you want to replicate, and define how frequently the data should refresh. The platform handles the data transformation and schema creation automatically, delivering structured time tracking information ready for SQL queries.

What Toggl data can I replicate to PostgreSQL?

You can replicate 10 different pipelines including Time Entries, Projects, Clients, Tasks, Tags, Groups, Workspaces, and user associations. Each pipeline contains specific fields such as duration in seconds, hourly rates, project status, and workspace IDs, giving you granular access to time tracking metrics. This comprehensive coverage allows you to analyze everything from individual task completion to client-level billing efficiency.

How often does Kondado update Toggl data in PostgreSQL?

Kondado updates your PostgreSQL database on a configurable schedule that you control, with options ranging from every 5 minutes to daily intervals. You select the frequency based on your analysis needs, whether you need near-real-time visibility for active project monitoring or daily batches for periodic reporting. The automated process ensures your time tracking data stays current without manual intervention.

Can I combine Toggl time tracking data with other sources in PostgreSQL?

Yes, once Toggl data resides in PostgreSQL, you can join it with information from other data sources such as accounting software, CRM systems, or additional project management tools. This enables cross-functional analysis like correlating time spent with invoice amounts or comparing Toggl hours against project budgets stored in other databases. You can also send this combined data to Power BI , Looker Studio , or Google Sheets for visualization.

What format does Toggl data take when replicated to PostgreSQL?

Toggl data replicates into PostgreSQL as structured relational tables with preserved data types, where time entries become timestamped records and projects appear as dimension tables with attributes like hourly rates and status. The schema maintains relationships between entities through foreign keys, such as linking time entries to specific projects and clients. This structure supports complex SQL joins and aggregations for custom reporting and analysis workflows.

Do I need coding skills to set up the Toggl PostgreSQL connection?

No coding is required to establish the connection between Toggl and PostgreSQL using Kondado's no-code platform. The interface guides you through authenticating your Toggl account and configuring your PostgreSQL destination using connection details. You simply select which pipelines to activate and set your preferred update schedule through the visual interface.

Can I analyze Toggl data in PostgreSQL using BI tools?

Yes, after replicating Toggl data to PostgreSQL, you can connect business intelligence tools like Power BI , Looker Studio , or BigQuery to create custom dashboards and reports. The structured data format in PostgreSQL supports complex SQL queries for calculating utilization rates, project profitability, and client billing efficiency. This flexibility allows both technical analysts and business users to derive actionable insights from time tracking metrics.

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Replicate Toggl Data to PostgreSQL Automatically

Connect your time tracking information to a powerful analytical database using Kondado’s no-code platform. To send Toggl data to PostgreSQL, authenticate your Toggl data source and select PostgreSQL as your destination. The platform replicates your time entries, projects, and client information on a configurable schedule, eliminating manual CSV exports and enabling immediate SQL-based analysis.

Kondado provides a direct integration that automatically replicates Toggl time tracking data to PostgreSQL on a configurable schedule ranging from every 5 minutes to daily, making time entry records, project details, and workspace information available for SQL analysis and business intelligence workflows.

Once your data arrives in PostgreSQL, you can combine Toggl time entries with financial data from accounting systems or project management metrics from other platforms. This enables agency analysts to calculate accurate client profitability, track team utilization rates, and generate custom reports using SQL queries or business intelligence tools like Power BI and Looker Studio.

Our prices start from $19 USD/month, and you can try Kondado for free for 14 days with no credit card required

Kondado offers 10 distinct pipelines including Time Entries, Projects, and Clients, allowing you to analyze exactly what you need. The Time Entries pipeline captures duration in seconds and descriptions, enabling precise billing calculations and productivity analysis directly in your database. Combine this with the Projects pipeline containing hourly rates and actual hours logged, plus the Clients pipeline with workspace associations, to build comprehensive profitability dashboards that show which clients and projects generate the most revenue per hour worked.

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Toggl data available for PostgreSQL

available pipelines

141

extractable fields

Available integrations

Integration	Description
Clients	Includes fields such as client ID, name, and workspace ID, enabling efficient client management.
Groups	Contains information about group ID, name, and list of workspaces, facilitating project organization.
Projects	Presents data such as project ID, actual hours, status, and hourly rate, essential for tracking activities.
Projects: Groups	Includes unique identifiers for projects and groups, facilitating the association between them.
Projects: Users	Provides data such as user ID, labour cost, and hourly rate, allowing analysis of user performance in projects.
Tags	Contains information about tag ID, name, and workspace ID, facilitating the categorization of entries.
Tasks	Includes data such as task ID, status, and update timestamp, aiding in activity management.
Time Entries	Presents information such as entry ID, description, and duration in seconds, essential for time tracking.
Workspaces	Contains data about workspace ID and name, allowing for the organization of projects and teams.
Workspaces: Users	Includes information such as user ID, name, and workspace ID, facilitating user management across different workspaces.