How often does Kondado update Zendesk data in MySQL?

Updates occur on a configurable schedule that you set during setup, ranging from every 5 minutes to daily intervals. The platform checks for new or modified records in your Zendesk account and replicates changes to MySQL accordingly. You can adjust the frequency anytime based on your analytics needs and data volume.

What Zendesk data pipelines are available for MySQL replication?

Kondado offers 12 distinct pipelines including Tickets, Users, Ticket Metrics, Ticket Comments, Satisfaction Surveys, Organizations, Groups, Tags, Ticket History, Jira Links, Ticket Metrics Events, and Closed and Deleted Tickets. Each pipeline contains specific fields such as ticket IDs, timestamps, custom fields, and metadata that map to corresponding structures in your MySQL database.

Can I combine Zendesk data with other sources in MySQL?

Yes, you can join Zendesk tables with data from BigQuery , PostgreSQL , or Google Sheets after replicating everything to MySQL. This enables comprehensive analysis such as correlating support ticket volumes with sales figures or marketing campaign performance using standard SQL joins.

How does Kondado handle deleted tickets in Zendesk?

The Closed and Deleted Tickets pipeline specifically captures records that have been removed from active Zendesk views, including deletion timestamps and actor information. This ensures your MySQL database maintains complete historical accuracy for compliance reporting and audit trails without losing visibility into previously deleted support interactions.

What format does Zendesk data take in MySQL?

Data arrives as structured relational tables with preserved data types, where each pipeline corresponds to a specific schema. Ticket Metrics and Satisfaction Surveys appear as numeric and categorical data ready for aggregation, while Ticket Comments and text fields maintain their original content for full-text search and sentiment analysis applications.

Can I use Zendesk data in MySQL with BI tools?

Absolutely, MySQL serves as a central hub where you can connect Power BI , Looker Studio , or Excel to create custom reports. The standardized structure allows these tools to query ticket trends, agent productivity, and customer satisfaction metrics directly from your database without API rate limits.

Does Kondado replicate custom fields from Zendesk tickets?

Yes, custom field data is included within the Tickets pipeline and maps to dedicated columns in your MySQL tables. This allows you to filter and segment reports by specific business categories, priority levels, or product areas that your team tracks using Zendesk's custom ticket field functionality.

How do I start analyzing Zendesk ticket metrics in MySQL?

Once the Ticket Metrics pipeline is active, you can query fields like first reply time, full resolution time, and requester wait time using standard SQL. Create calculated fields to measure SLA compliance percentages or build automated alerts when average response times exceed your internal thresholds.

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Send Zendesk Data to MySQL Automatically

How do you send Zendesk data to MySQL? Kondado provides a direct integration that replicates your Zendesk support data to MySQL on a configurable schedule. Set up your data source once, choose your update frequency from every 5 minutes to daily, and let the platform handle the rest. Your ticket information, customer details, and support metrics flow continuously into your database without manual exports or complex coding.

Kondado automatically replicates 12 data pipelines from Zendesk to MySQL, including Tickets, Users, Ticket Metrics, and Satisfaction Surveys, updating on schedules ranging from 5 minutes to daily intervals.

Once your data arrives in MySQL, you can build custom reports, combine support metrics with sales data from other sources, or power operational dashboards. The replication process preserves data relationships, allowing you to analyze ticket lifecycles, agent performance, and customer satisfaction trends using standard SQL queries. Whether you are an analyst creating executive summaries or a developer building customer-facing features, having Zendesk data in MySQL unlocks flexible analytics capabilities.

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Replicate the Tickets and Ticket Metrics pipelines to analyze resolution times and SLA compliance directly in MySQL. Combine Ticket Comments data with user information to build sentiment analysis reports that track customer satisfaction trends over time. The Closed and Deleted Tickets pipeline ensures you maintain historical records for compliance reporting and trend analysis.

With Satisfaction Surveys data in your database, you can correlate support quality with business outcomes by joining scores against transaction data from BigQuery or PostgreSQL. Organizations and Groups data enable segmentation analysis, helping you identify which support teams deliver the fastest response times and highest customer satisfaction rates.

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Zendesk data available for MySQL

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 MySQL

Sync data automatically — no code, no manual exports.

Connect Zendesk on Kondado

Authenticate your Zendesk account by providing your API credentials and subdomain details in the Kondado interface. The platform will validate access to your support data and display available pipelines for replication.

Configure MySQL destination

Enter your MySQL connection parameters including host, port, database name, and authentication credentials. Kondado tests the connection to ensure write permissions are properly configured for creating tables and inserting Zendesk data.

Select pipelines and schedule

Choose which of the 12 available pipelines to replicate, such as Tickets, Users, or Ticket Metrics, then set your preferred update frequency from 5 minutes to daily. The system begins the initial load immediately and continues automated updates according to your schedule.

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