Infoveave Data Automation — Text & String
Every key-value pair in your URL query strings becomes its own named column — UTM parameters, session IDs, filter values, and API parameters all land in structured columns automatically.
Marketing campaigns embed tracking parameters — utm_source, utm_medium, utm_campaign — into links as URL query strings. APIs log request parameters as query strings. Filter states get saved as URL parameters. In all these cases, the analysis-relevant data is encoded inside a single URL column as a compressed key-value list. Split HTTP Query decodes each parameter into its own named column, enabling direct filtering, aggregation, and join operations on individual parameter values without any string parsing logic. Combine with Split URL to handle full URLs, or feed raw query strings directly when the query string is already isolated in a column.
Extract individual HTTP query string key-value pairs from URL columns into named columns in Infoveave. Turn raw query strings like id=E001&name=John into Key_id and Key_name columns for campaign tracking, session analysis, and API log parsing.
Split HTTP Query is one step inside a multi-step Infoveave workflow. Chain it with other activities — no code, no manual hand-offs.
Build this workflow visually in Infoveave Data Automation — drag, connect, and schedule with no infrastructure setup.
Real scenarios where this transformation saves hours of manual work.
A marketing analytics team processes a campaign performance dataset where each click record includes the full landing URL with embedded UTM tracking parameters. After using Split URL to isolate the query string, Split HTTP Query expands the query into separate Key_utm_source, Key_utm_medium, Key_utm_campaign, Key_utm_content, and Key_utm_term columns. The team can then group click and conversion metrics directly by campaign, medium, and source without any string manipulation.
A platform team processes API gateway logs where each request record includes the full request URL with query string parameters encoding the request context — user ID, resource type, version, and filter values. Split HTTP Query extracts each parameter into a named column, allowing the team to analyze API usage patterns by user, filter by resource type, group response latency by API version, and identify over-used parameter combinations from column-level aggregation.
A product analytics team analyzes site session data where filter selections are encoded as URL query strings in the page URL column — for example category=electronics&brand=HP&price_range=500-1000. Split HTTP Query extracts each filter parameter into a named column per key. The team can count how often each filter combination is applied, identify which filter combinations correlate with purchase conversion, and rank the most used filter values per category.
Input data (left) is transformed using the configuration below. The output table (right) is ready for dashboards or downstream steps.
urlKey_YesInput Data
| Employee ID | Name | url |
|---|---|---|
| E001 | John Doe | https://company.com/employee?id=E001&name=John+Doe&department=Sales |
| E002 | Marie Dupont | https://marketing.com/employee?id=E002&name=Marie+Dupont&department=Marketing |
| E003 | Carlos Gomez | https://fileserver.com/download?id=E003&name=Carlos+Gomez&department=IT |
Output Data
| Employee ID | Name | url | Key_id | Key_name | Key_department |
|---|---|---|---|---|---|
| E001 | John Doe | https://company.com/employee?id=E001&name=John+Doe&department=Sales | E001 | John+Doe | Sales |
| E002 | Marie Dupont | https://marketing.com/employee?id=E002... | E002 | Marie+Dupont | Marketing |
| E003 | Carlos Gomez | https://fileserver.com/download?id=E003... | E003 | Carlos+Gomez | IT |
Key fields to configure in the Infoveave workflow builder. Full reference available in the documentation.
Column Name
Select the column that contains either full URLs or raw query strings. Split HTTP Query automatically finds the query string portion of a full URL, so you can pass a full URL column directly without first isolating the query. You can also pass a column that contains only the query string portion — either starting with a question mark or without it — and the parsing works the same way.
Prefix
Define the prefix added to each output column name. The column name for each parameter becomes the prefix followed by the parameter key — for example a prefix of Key_ and parameter id produces column Key_id. Using a consistent prefix prevents naming conflicts with existing dataset columns and makes auto-generated query parameter columns easy to identify and filter in downstream steps.
Include Original
Choose whether to retain the original URL or query string column alongside the newly generated parameter columns. Keep it on if you need the complete URL for debugging, as a display value, or for passing to additional transformations. Disable it to keep the output clean when the original URL column is no longer needed after parameter extraction.
Everything you need to know about Split HTTP Query in Infoveave.
Transformations in the same family as Split HTTP Query, often chained together in the same Infoveave workflow.
Part of Infoveave Data Automation
Split HTTP Query is one of over 80 transformation activities available inside Infoveave workflows. Chain transformations together — no code, no exports, no waiting for IT.
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