How does Kondado replicate Zendesk data to Amazon S3?

Kondado connects to your Zendesk account using a direct integration, extracts data from your selected pipelines, and loads it into your specified Amazon S3 bucket on a configurable schedule. The platform handles schema mapping and data type conversion automatically, ensuring your ticket information arrives in a structured format ready for analysis.

What Zendesk pipelines are available for Amazon S3 replication?

You can activate pipelines including Tickets, Users, Ticket Comments, Ticket Metrics, Satisfaction Surveys, Organizations, Groups, Ticket History, and Closed and Deleted Tickets. Each pipeline contains specific fields such as ticket IDs, timestamps, custom fields, and user metadata that replicate to your S3 storage.

How often does Zendesk data update in Amazon S3?

Update frequency is fully configurable based on your business needs, with options ranging from every 5 minutes to daily schedules. You select the sync interval when setting up your pipeline, and Kondado automatically manages the data extraction and loading process according to your timeline.

What file format does Zendesk data use in Amazon S3?

Data loads into Amazon S3 in Parquet or CSV format, depending on your configuration, creating partitioned files that optimize query performance when using analytics engines. This structure allows efficient data scanning when connecting to visualization tools like Power BI or BigQuery .

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

Yes, you can replicate data from multiple sources alongside Zendesk into the same Amazon S3 environment, creating a unified data lake. Combine your support data with CRM information or marketing metrics to build comprehensive reports that span across departments.

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

Once stored in Amazon S3, you can query Zendesk data using Amazon Athena, Presto, or Dremio directly on the files, or load it into PostgreSQL for traditional SQL analysis. Many users connect their S3 data to Looker Studio or Power BI to create interactive dashboards without moving the data.

Does Kondado replicate deleted Zendesk tickets to Amazon S3?

Yes, the Closed and Deleted Tickets pipeline specifically captures tickets that have been removed from your active Zendesk instance, including deletion timestamps and previous status information. This allows you to maintain historical records and audit trails for compliance or analysis purposes even after tickets are purged from the source system.

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Send Zendesk Data to Amazon S3 Automatically

Kondado provides a direct integration between Zendesk and Amazon S3, allowing you to replicate your customer service data to scalable cloud storage without writing code. You configure which pipelines to activate from the 12 available options, set your preferred update frequency, and Kondado handles the automated data transfer on a configurable schedule. This setup enables you to centralize ticket information, user records, and satisfaction scores alongside other business data in your Amazon S3 data lake for comprehensive analysis.

Kondado replicates data from Zendesk to Amazon S3 through 12 available pipelines covering 235 fields, including Tickets, Users, Ticket Comments, and Satisfaction Surveys, with automated updates running every 5 minutes, 15 minutes, hourly, or daily based on your configuration.

Once your Zendesk data lands in Amazon S3, you can query it directly using Amazon Athena, Presto, or Dremio, or connect it to business intelligence tools like Power BI or Looker Studio for advanced visualization. This workflow transforms raw support tickets and customer interactions into actionable insights that drive strategic decisions across your organization.

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Replicate the Ticket Metrics pipeline to track response times and resolution efficiency directly within your Amazon S3 environment, enabling you to build custom dashboards that monitor SLA compliance across support teams. Combine Ticket Comments data with Satisfaction Surveys scores to analyze sentiment trends and correlate customer feedback with specific support interactions stored in your data lake. You can also sync the Organizations pipeline to segment performance metrics by client company, creating detailed reports in Power BI or Looker Studio that show ticket volume and resolution quality for each business unit without manual data exports.

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Zendesk data available for Amazon S3

available pipelines

235

extractable fields

Available integrations

Integration	Description
Closed and Deleted Tickets	Includes deleted tickets with fields like id, actor_id, and deleted_at, allowing analysis of previous status and ticket description.
Ticket Comments	Contains comments related to tickets, with fields like ticket_id, author_id, and created_at, along with the comment body.
Ticket Metrics Events	Records SLA metric events, including id, metric, and sla_target, allowing performance monitoring of tickets.
Groups	Lists groups with fields like id, name, and updated_at, along with information about the group's status and description.
Ticket History	Records ticket audits with fields like id, ticket_id, and created_at, along with metadata about the source and channel.
Jira Links	Information about links between tickets and Jira tasks, allowing integration and tracking of related tasks.
Tags	Contains information about tags associated with tickets, facilitating categorization and search of tickets by tag.
Ticket Metrics	Presents ticket performance metrics, including response time and resolution time, essential for efficiency analysis.
Organizations	Lists organizations with fields like id, name, and created_at, allowing management and categorization of tickets by organization.
Satisfaction Surveys (Scores)	Collects satisfaction survey results, including id, ticket_id, and score, essential for measuring customer satisfaction.
Tickets	Contains information about tickets, including id, status, and timestamps such as created_at and deleted_at.
Users	Stores data about users, including id, name, and contact information such as email and status.

Closed and Deleted Tickets

Includes deleted tickets with fields like id, actor_id, and deleted_at, allowing analysis of previous status and ticket description.

Ticket Comments

Contains comments related to tickets, with fields like ticket_id, author_id, and created_at, along with the comment body.

Ticket Metrics Events

Records SLA metric events, including id, metric, and sla_target, allowing performance monitoring of tickets.

Groups

Lists groups with fields like id, name, and updated_at, along with information about the group's status and description.

Ticket History

Records ticket audits with fields like id, ticket_id, and created_at, along with metadata about the source and channel.

Jira Links

Information about links between tickets and Jira tasks, allowing integration and tracking of related tasks.

How to send Zendesk data to Amazon S3

Sync data automatically — no code, no manual exports.

Connect your Zendesk data source

Authenticate your Zendesk account in Kondado by providing your API credentials, allowing the platform to access your support data for replication.

Configure your Amazon S3 destination

Enter your AWS bucket details and authentication keys to establish the S3 location where your Zendesk pipelines will store data files.

Select pipelines and schedule updates

Choose which of the 12 available pipelines to activate, such as Tickets or Ticket Metrics, and set your preferred update frequency from 5 minutes to daily intervals.

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