Send data from Clockify to Amazon S3

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Send Clockify Data to Amazon S3 Automatically

Looking to centralize your time tracking data for advanced analytics? Kondado provides a direct integration between Clockify and Amazon S3, allowing you to replicate workforce metrics on a configurable schedule. Simply connect your Clockify account, configure your Amazon S3 bucket, and select which data pipelines you want to replicate. Your time entries, project details, and workspace configurations will flow automatically into your data lake, ready for analysis with your preferred query engines. This automated approach eliminates manual CSV exports and ensures your S3 data lake always contains current project hours and billing information for comprehensive workforce analysis.

Kondado replicates Clockify Workspaces and Projects pipelines to Amazon S3 on a configurable schedule ranging from every 5 minutes to daily, delivering 115 fields of time tracking data in a format optimized for Athena, Presto, and Dremio analytics. The platform handles authentication, data transformation, and file organization automatically, requiring no manual exports or coding to maintain fresh time tracking records in your cloud storage.

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

Available Clockify Pipelines for Amazon S3

With Kondado, you can replicate the Workspaces pipeline to capture organizational settings including week start times and hourly rates alongside admin permissions. The Projects pipeline delivers detailed project metadata such as billable status, color coding, and time estimates directly into your Amazon S3 storage. Once stored, you can combine this Clockify data with financial records from other sources to calculate project profitability, or blend it with HR data to analyze team productivity trends across your entire organization. These pipelines enable you to track billable hours against project budgets and monitor workspace utilization patterns without writing custom API scripts, providing immediate access to structured time tracking data for your analytics workflows.

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Clockify data available for Amazon S3

2
available pipelines
115
extractable fields

Available integrations

Integration Description
Workspaces Includes fields such as id, name, and admin permissions, along with settings like week start time and hourly rate.
Projects Contains information such as id, name, color, and whether the project is billable, along with details about time estimate and hourly rate.
Workspaces
Includes fields such as id, name, and admin permissions, along with settings like week start time and hourly rate.
Projects
Contains information such as id, name, color, and whether the project is billable, along with details about time estimate and hourly rate.
See all available data →

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How to send Clockify data to Amazon S3

Sync data automatically — no code, no manual exports.

1
Connect Your Clockify Account

Enter your Clockify API credentials in Kondado to establish authentication for your Clockify data source and authorize access to your workspaces and projects.

2
Configure Amazon S3 Settings

Specify your target bucket name, folder path, and file format preferences to determine exactly where and how your Clockify data will be organized within your Amazon S3 environment.

3
Select Pipelines and Schedule

Choose the Workspaces and Projects pipelines you want to replicate, then set your update frequency to run every 5 minutes, hourly, or daily based on your reporting needs.

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Send data from Clockify to other destinations

Looker Studio

Clockify > Looker Studio
Power BI

Clockify > Power BI
Other visualizations
Other visualizations
Amazon S3

Clockify > Amazon S3
BigQuery

Clockify > BigQuery
Excel

Clockify > Excel
Google Sheets

Clockify > Google Sheets
MySQL

Clockify > MySQL
PostgreSQL

Clockify > PostgreSQL
Redshift

Clockify > Redshift
SQL Server

Clockify > SQL Server

Choose a tool to visualize your Clockify data

If the software you need is not listed, drop us a messagem. You can use almost every tool

Airtable
Clockify > Airtable
Alteryx
Clockify > Alteryx
Amazon Quicksight
Clockify > Amazon Quicksight
BIMachine
Clockify > BIMachine
Cortex
Clockify > Cortex
Data Studio
Clockify > Data Studio
Geckoboard
Clockify > Geckoboard
Grafana
Clockify > Grafana
IBM Cognos Analytics
Clockify > IBM Cognos Analytics
Klipfolio
Clockify > Klipfolio
Looker Studio
Clockify > Looker Studio
Looker
Clockify > Looker
Looqbox
Clockify > Looqbox
Metabase
Clockify > Metabase
Microstrategy
Clockify > Microstrategy
Pentaho
Clockify > Pentaho
Power BI
Clockify > Power BI
Qlik Cloud Analytics
Clockify > Qlik Cloud Analytics
Qlik Sense
Clockify > Qlik Sense
Qlik
Clockify > Qlik
QlikView
Clockify > QlikView
Redash
Clockify > Redash
Superset
Clockify > Superset
Tableau
Clockify > Tableau
BI TOTVS
Clockify > BI TOTVS
Zoho Analytics
Clockify > Zoho Analytics

Frequently Asked Questions (FAQ)

Answers about sending Clockify data to Amazon S3 automatically

What file format does Clockify data use in Amazon S3? ▼
Kondado delivers Clockify data to Amazon S3 in Parquet or CSV format, structured for immediate querying with Athena or Presto. Each pipeline creates separate files organized by extraction timestamp, making it simple to build historical time tracking datasets. The columnar format ensures efficient storage and fast query performance when analyzing project hours and billable rates.
How often can I update Clockify data in my S3 bucket? ▼
You can configure updates to run every 5 minutes, 15 minutes, hourly, or daily depending on your analytics requirements. This flexibility allows agencies to monitor active project hours throughout the day while enterprises may prefer daily consolidation for financial reporting. The schedule applies independently to each pipeline, so you can update Projects frequently while keeping Workspaces on a slower cadence.
Can I combine Clockify data with other sources in Amazon S3? ▼
Absolutely, Amazon S3 serves as a central repository where Clockify time tracking data can coexist with CRM records, financial databases, or HR systems. You can join these datasets using Athena or Dremio to create comprehensive reports showing the relationship between hours worked and revenue generated. This approach enables custom profitability dashboards that blend time entries with invoicing data from other business applications.
What specific Clockify fields are available in the Projects pipeline? ▼
The Projects pipeline includes 115 fields covering project identifiers, names, color codes for visual organization, and billable status flags. You will also find detailed financial settings including hourly rates and time estimates that help calculate project budgets and resource allocation. These fields enable precise tracking of billable versus non-billable hours across your entire portfolio.
Do I need technical skills to set up the Clockify to Amazon S3 connection? ▼
No coding is required to establish this connection, as Kondado provides a guided interface for authenticating your Clockify account and specifying your S3 bucket details. The platform handles API authentication, data transformation, and file delivery automatically after you select your desired pipelines. Technical users can customize file naming conventions and folder structures, but the basic setup requires only your Clockify API key and AWS credentials.
Can I send Clockify data to destinations other than Amazon S3? ▼
Yes, Kondado supports multiple destinations including BigQuery, PostgreSQL, Power BI, Google Sheets, and Looker Studio. You can replicate the same Clockify pipelines to different destinations simultaneously, maintaining consistent data across your analytics stack. This flexibility allows you to feed time tracking data into data warehouses for SQL analysis while also updating spreadsheets for operational reviews.
How does Kondado handle Clockify API rate limits during replication? ▼
Kondado implements intelligent request throttling that respects Clockify's API limitations while maximizing data throughput within allowed boundaries. The platform queues extraction requests and implements retry logic with exponential backoff if rate limits are approached during large historical syncs. This ensures reliable delivery of your 115 fields without manual intervention or failed extractions.

Try out all the features for free for 14 days