How does Kondado replicate Tangerino data to Amazon S3?

Kondado connects directly to your Tangerino account and extracts data from the Daily Summary and Employees pipelines. The platform loads this information into your specified Amazon S3 bucket on a configurable schedule you control, handling the data transfer without requiring custom scripts or manual exports.

What specific Tangerino data fields are available in Amazon S3?

You can access 153 fields across two pipelines. The Daily Summary includes worked hours, paid hours, compensatory hours, employeeid, and isholiday flags. The Employees pipeline contains name, email, admission date, birthdate, and jobroledto__id for comprehensive workforce analysis.

How often does Tangerino data update in my Amazon S3 bucket?

Kondado updates your S3 data on a configurable schedule that you set during setup. Choose intervals ranging from every 5 minutes for near-real-time visibility to hourly or daily updates depending on your analytics requirements and data freshness needs.

What file format does Tangerino data use when stored in Amazon S3?

Data arrives in Amazon S3 in a structured format optimized for analytics engines like Athena, Presto, and Dremio. This allows you to run SQL queries directly against your Tangerino attendance and employee files without additional transformation or data type conversions.

Can I combine Tangerino data with other HR systems in my Amazon S3 data lake?

Yes, you can blend Tangerino time tracking data with information from additional sources in the same S3 environment. Combine attendance records with payroll data from BigQuery exports or employee benefits data from PostgreSQL to create comprehensive workforce analytics.

How do I query Tangerino attendance data once it is in Amazon S3?

Use Amazon Athena, Presto, or Dremio to run SQL queries directly against your Tangerino data files in S3. These tools let you analyze Daily Summary records to calculate overtime trends, filter by holiday flags, or join employee metadata with time entries for departmental reporting.

What can I build with Tangerino data in Amazon S3?

You can construct custom HR dashboards, automated payroll verification systems, and labor cost analysis reports. Feed your S3 data into visualization tools like Power BI or Looker Studio to display attendance patterns, track employee hours, and monitor workforce productivity across your organization.

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Replicate Tangerino Data to Amazon S3

How do you send Tangerino data to Amazon S3 without writing code? Kondado provides a direct integration that replicates your employee time tracking and attendance information to Amazon S3 on a configurable schedule. Set up automated updates every 5 minutes, hourly, or daily to keep your data lake current without manual CSV exports or API development. Once your Tangerino data lands in Amazon S3, you can query attendance records using Amazon Athena, join with payroll information from other data sources, or feed business intelligence tools for comprehensive workforce analytics.

Kondado connects to Tangerino and replicates Daily Summary and Employees pipelines to Amazon S3 automatically on your chosen schedule, delivering 153 fields of workforce data ready for analysis with Athena, Presto, or Dremio.

With Kondado, you eliminate the need for manual data pulls from your time clock system. The platform handles the extraction and loading of Tangerino attendance records into your S3 buckets, ensuring your data virtualization layer always has fresh information for HR dashboards and labor cost analysis.

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

Available Tangerino Pipelines for Amazon S3

The Daily Summary pipeline delivers worked hours, paid hours, and compensatory hours alongside employee identifiers and holiday flags, enabling you to build detailed attendance compliance dashboards directly in Amazon S3. The Employees pipeline brings essential workforce metadata including names, emails, admission dates, and job role identifiers that let you enrich time tracking data with full organizational context. Together, these pipelines empower you to calculate accurate labor costs by department, identify overtime patterns using Amazon Athena queries, and create unified HR reports by combining Tangerino attendance information with payroll and ERP data from other sources stored in your S3 data lake.

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Tangerino data available for Amazon S3

available pipelines

153

extractable fields

Available integrations

Integration	Description
Daily Summary	Includes information such as worked hours, paid hours, and compensatory hours. Fields like id, employeeid, and isholiday help analyze employee attendance.
Employees	Contains data about employees, including name, email, and admission date. Fields like id, jobroledto__id, and birthdate provide essential details about each employee.

Daily Summary

Includes information such as worked hours, paid hours, and compensatory hours. Fields like id, employeeid, and isholiday help analyze employee attendance.

Employees

Contains data about employees, including name, email, and admission date. Fields like id, jobroledto__id, and birthdate provide essential details about each employee.

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How to send Tangerino data to Amazon S3

Sync data automatically — no code, no manual exports.

Connect Tangerino Data Source

Authenticate your Tangerino account in Kondado by providing your API credentials. Select Tangerino from the data source catalog to establish the initial connection for your time tracking data.

Configure Amazon S3 Destination

Enter your Amazon S3 bucket details and specify the file path where Tangerino data should land. Set the storage format compatible with your analytics stack, whether you plan to query with Athena or process with other AWS services.

Select Pipelines and Schedule

Choose the Daily Summary and Employees pipelines you want to replicate, then configure your update frequency. Select intervals from 5 minutes to daily based on how current you need your attendance and employee data for reporting.

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