How do I connect Toggl to Amazon S3 using Kondado?

Start by adding Toggl as a data source using your API credentials, then configure your Amazon S3 bucket as the destination by providing your access keys and bucket details. Once connected, select the specific pipelines you want to replicate and set your preferred update schedule.

What Toggl data can I replicate to Amazon S3?

Kondado offers 10 pipelines including Time Entries, Projects, Clients, Tasks, Tags, Groups, Workspaces, Projects: Users, and Workspaces: Users. This covers 141 fields ranging from time durations and hourly rates to project statuses and client information.

How often does Kondado update Toggl data in Amazon S3?

You can configure automated updates to run every 5 minutes, 15 minutes, hourly, or daily depending on your analytics needs. This ensures your S3 bucket contains current time tracking data without requiring manual refreshes or exports.

What file format does Toggl data arrive in when sent to Amazon S3?

Data is delivered in Parquet or CSV format optimized for analytics engines like AWS Athena, Presto, and Dremio. The structured format allows you to run complex SQL queries directly on your time tracking data without preprocessing.

Can I combine Toggl data with other sources in Amazon S3?

Yes, you can replicate data from multiple sources into the same S3 bucket or data lake, then join Toggl time entries with CRM data, financial records, or project management information. You can also send Toggl data to BigQuery , PostgreSQL , or Google Sheets for unified reporting.

Do I need coding skills to send Toggl data to Amazon S3?

No coding is required to set up the connection or schedule automated replications. Kondado provides a no-code interface where you authenticate your Toggl workspace and configure your S3 destination through guided steps.

Can I track billable hours across multiple Toggl workspaces in Amazon S3?

Yes, by replicating the Workspaces, Workspaces: Users, and Time Entries pipelines, you can aggregate time tracking data from multiple Toggl workspaces into a single S3 repository. This enables consolidated reporting on billable hours and project costs across your entire organization.

Send data from Toggl to Amazon S3

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Send Toggl Data to Amazon S3 Automatically

Connecting Toggl to Amazon S3 enables you to centralize time tracking data for advanced analytics and long-term storage. With Kondado, you can replicate your Toggl time entries, projects, and client data directly to Amazon S3 without writing code or managing complex API connections manually. Simply authenticate your Toggl workspace, configure your S3 bucket settings, and select the specific pipelines you want to replicate on a schedule that fits your business needs, from near-real-time updates to daily batches.

Kondado automatically replicates Toggl data to Amazon S3 on a configurable schedule ranging from every 5 minutes to daily, making your time tracking information available for analysis with AWS Athena, Presto, or Dremio without manual CSV exports or custom scripts.

Once your data lands in Amazon S3, you can query it using standard SQL through AWS Athena, combine it with CRM or financial data from other sources, or feed it into business intelligence tools like Power BI or Looker Studio to build custom productivity dashboards that track billable hours across projects and clients.

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The Time Entries pipeline captures detailed records of every tracked session including duration, descriptions, and timestamps, enabling you to analyze productivity patterns and billable hours using SQL queries in Athena. Combined with the Projects pipeline, which contains project budgets, statuses, and hourly rates, you can calculate project profitability and resource allocation directly within your S3 data lake. The Clients and Workspaces: Users pipelines bring in client information and team member assignments, allowing you to generate comprehensive financial reports that aggregate time spent across multiple projects for each customer while monitoring individual contributor performance across different teams.

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Toggl data available for Amazon S3

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.

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.

How to send Toggl data to Amazon S3

Sync data automatically — no code, no manual exports.

Connect Your Toggl Account

Authenticate your Toggl workspace by entering your API token in Kondado's data source configuration page to establish the initial connection.

Configure Amazon S3 Destination

Enter your AWS access credentials and specify your target S3 bucket name and folder path where the Toggl data will be stored in Parquet format.

Select Pipelines and Schedule

Choose from the 10 available Toggl pipelines such as Time Entries and Projects, then set your replication frequency to run every 5 minutes, hourly, or daily based on your reporting needs.

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