What specific Webhook data can I extract using Kondado?

Kondado extracts raw JSON payloads through the Post raw JSON pipeline, capturing __kdd_request_id for unique identification, __kdd_request_unix_timestamp for temporal analysis, and __kdd_request_body containing the complete event data. This structure supports logging from any webhook source regardless of payload complexity or event type.

How do I connect Webhook to Google Sheets for event tracking?

You can replicate Webhook data directly to Google Sheets by selecting it as your destination during setup, enabling event logs to populate spreadsheets for immediate team access. This configuration supports automated refreshes so your event tracking stays current without manual imports.

What update frequency options are available for Webhook data replication?

Kondado offers configurable scheduling options ranging from near-real-time updates every 5 minutes to daily batches, allowing you to balance data freshness with processing requirements. Marketing teams often prefer 5-minute intervals for campaign monitoring, while operational reporting may suit hourly or daily schedules.

Can I analyze Webhook data in Power BI or Looker Studio?

Yes, you can send Webhook data to Power BI or Looker Studio to create custom dashboards visualizing event trends, request volumes, and temporal patterns. These connections enable you to build reports showing webhook activity alongside other business metrics.

How does the Post raw JSON pipeline handle different Webhook API payload structures?

The Post raw JSON pipeline stores the complete request body as JSON, preserving the original structure of your webhook payloads regardless of whether they contain customer data, transaction records, or system notifications. This flexibility allows you to parse nested objects and arrays according to your specific analytical needs.

What types of KPIs can I monitor with Webhook analytics?

Common KPIs include event throughput rates calculated from request ID counts, latency analysis using timestamp differentials, and payload size trends to monitor data volume growth. You can also track geographic patterns or error frequencies by analyzing the content within request bodies.

Is it possible to send Webhook data to PostgreSQL or BigQuery?

Absolutely, Kondado supports sending Webhook data to PostgreSQL for relational database analysis or BigQuery for data warehouse storage and large-scale querying. Both destinations accommodate the JSON structure for flexible schema evolution.

How do I set up multiple webhook endpoints for different event types?

While the current pipeline captures all incoming webhook traffic to a single endpoint, you can filter and route different event types during the configuration process by setting up distinct pipeline instances. Each instance can process specific JSON structures and send them to different destinations like MySQL, Redshift, or SQL Server.

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Connect Webhook Data to Dashboards and Databases

Webhook enables event-driven data flows by pushing JSON payloads to endpoints whenever specific actions occur in your applications, from e-commerce transactions to user signups. Through Kondado, you can capture these incoming webhook requests and replicate the raw JSON data for comprehensive analysis across your business intelligence stack. The platform logs every request with unique identifiers, timestamps, and the complete request body, allowing you to track events as they happen across your SaaS tools, custom applications, and third-party services.

Kondado provides direct integration with Webhook through 1 pipeline containing 4 fields including __kdd_request_id, __kdd_request_unix_timestamp, and __kdd_request_body. This enables comprehensive logging of all incoming webhook requests in JSON format for downstream analysis.

Marketing teams can monitor campaign events and user interactions by analyzing webhook request patterns and timestamps to optimize conversion funnels. Data analysts benefit from having structured JSON event data available in SQL databases or BI tools for complex querying, cohort analysis, and trend identification. Operations teams use this data source to track system health metrics, monitor API call volumes, and create automated alerts based on incoming webhook frequency or specific payload characteristics that indicate system performance issues.

The Kondado platform takes care of refreshing Webhook data, allowing you to stop wasting time with manual work and complex workflows, and focus on analyzing Webhook data in your report, spreadsheet, data warehouse, data lake, or database

Available Pipelines for Webhook

The Post raw JSON pipeline captures comprehensive webhook request details for your analytics needs. This pipeline stores unique request identifiers that enable precise event tracking across distributed systems, while the timestamp fields allow chronological analysis of when specific events occurred in your applications. The request body field preserves complete JSON payloads containing customer actions, transaction details, or system events depending on your webhook configuration, enabling deep inspection of event data.

You can combine webhook event data with CRM records or transaction databases to correlate external events with internal business metrics and customer behaviors. Cross-referencing request timestamps with sales data reveals how external events correlate with revenue patterns and operational efficiency. With automated updates available on schedules ranging from every 5 minutes to daily, you can monitor KPIs like event throughput, response latency trends, or error rates as frequently as your business requires.

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Available Webhook data

available pipeline

extractable fields

Available integrations

Integration	Description
Post raw JSON	This table includes fields such as __kdd_request_id, __kdd_request_unix_timestamp, and __kdd_request_body, enabling the logging of requests and their data in JSON format.

Post raw JSON

This table includes fields such as __kdd_request_id, __kdd_request_unix_timestamp, and __kdd_request_body, enabling the logging of requests and their data in JSON format.

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