Find Text
Infoveave Data Automation — Text & String
Write the regex pattern once — Infoveave extracts the matching tokens from every text column row on every scheduled run.
Product descriptions, support ticket notes, log messages, and customer feedback fields frequently embed structured data — product codes, order IDs, error codes, hashtags, or numeric identifiers — inside free-form text. Extracting those structured pieces manually or with ad-hoc scripts is fragile and does not scale. Find Text extracts structured tokens from text columns using a regex pattern, placing each capture group match into its own new column. The result is a clean, structured dataset where embedded identifiers are now explicit columns that can be filtered, joined, and aggregated.
What Find Text does
Extract structured tokens, codes, keywords, and patterns from text columns in Infoveave using regular expressions. Parse product codes, IDs, hashtags, and structured substrings from unstructured text automatically.
When to use Find Text
- A text column contains embedded structured tokens — such as product codes, order IDs, error codes, or hashtags — that you need to extract as separate columns for filtering, joining, or reporting
- You are parsing semi-structured log messages, support ticket text, or event descriptions where known patterns repeat across rows but the exact position varies
- You want to validate and extract standard formats such as email addresses, postal codes, dates written as text, or numeric identifiers from a description or notes column
- You need to produce new structured columns from a single catch-all text field in a source system that stores multiple pieces of information in one column
When to avoid it
- You want to replace matching text rather than extract it — use Find and Replace for in-place value substitution
- You want to split the column into fixed-position columns by a delimiter — use Split Column for delimiter-based horizontal splitting
- The patterns you need to extract are not consistent enough for a regex — use Summarize with AI for unstructured natural language extraction that does not follow a clear pattern
Where it fits in your Infoveave automation
Find Text 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.
How teams use Find Text
Real scenarios where this transformation saves hours of manual work.
Extract Error Codes and Component IDs from Application Logs
A DevOps team processes application log records where each log message contains an error code pattern like ERR-1234 and a component identifier like COMP-56. Find Text uses a regex pattern to extract error codes and component IDs from the log message column into separate columns, allowing the team to filter by specific error codes, aggregate error frequency per component, and route alerts based on error type — without parsing the raw log text in every downstream query.
Parse Product Codes from Mixed Description Fields
A retail data team processes supplier product feeds where product codes like SKU-AB1234 are embedded inside a product description column along with free-form text. Find Text extracts the SKU code from each description into a dedicated product_code column using a regex pattern matching uppercase letters and digits with the SKU prefix. The extracted codes can then be joined against the inventory reference table without manual text parsing.
Extract Transaction Reference Numbers from Payment Notes
A financial operations team processes payment records where each payment notes field contains a transaction reference number in a defined format embedded within free-form text. Find Text extracts the reference number pattern into a dedicated column, enabling reconciliation against the external payment processor ledger by reference number without parsing the notes field in every reconciliation query.
See Find Text in action
Input data (left) is transformed using the configuration below. The output table (right) is ready for dashboards or downstream steps.
DescriptionOne or more uppercase letters (3 or more characters)Column_EnabledInput Data
| ID | Description |
|---|---|
| 1 | This contains ABC and XYZ |
| 2 | Find CODE inside this text |
| 3 | No pattern matches here |
| 4 | Extract INFO and DATA points |
| 5 | SAMPLE test for extraction |
Output Data
| ID | Description | Column_1 | Column_2 |
|---|---|---|---|
| 1 | This contains ABC and XYZ | ABC | XYZ |
| 2 | Find CODE inside this text | CODE | |
| 3 | No pattern matches here | ||
| 4 | Extract INFO and DATA points | INFO | DATA |
| 5 | SAMPLE test for extraction | SAMPLE |
Configuration
Key fields to configure in the Infoveave workflow builder. Full reference available in the documentation.
Pattern
A regular expression that defines what to extract from the text column. Use capturing groups within the pattern to capture each piece you want as a separate output column. Each capture group match produces one output column named using the prefix and a sequential number. For example, a pattern with two capture groups on a row that matches both groups produces two output columns per row.
Output Columns Prefix
All new columns generated from the regex matches are named using this prefix followed by a sequence number — for example Column_1, Column_2 for a prefix of Column_. Set a meaningful prefix that reflects what the extracted values represent, such as Code_ or ID_, to make the output columns self-describing.
Include Original
When enabled, the original source text column is retained alongside the new extracted columns. When disabled, only the extracted columns appear in the output. Retaining the original is useful for auditing the extraction results or for using the source text in subsequent parsing steps.
Frequently asked questions
Everything you need to know about Find Text in Infoveave.
Also in Text & String — and what runs before & after
Transformations in the same family as Find Text, often chained together in the same Infoveave workflow.
Part of Infoveave Data Automation
80+ transformations. Zero manual steps.
Find Text 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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