Drop Columns
Infoveave Data Automation — Column Operations
Pick the columns you do not need. Every run delivers a cleaner, leaner dataset with those fields gone.
Raw data from source systems always contains more columns than any downstream step actually needs — internal system IDs, nested JSON blobs, audit metadata, sensitive identifiers, and fields generated by the source tool for its own use. Drop Columns removes those unwanted fields at the start of your workflow, reducing data volume, eliminating irrelevant noise from reports, and preventing sensitive fields from traveling further along the pipeline than they should.
What Drop Columns does
Remove specific columns from a dataset in Infoveave workflows. Clean up JSON metadata, system fields, and sensitive data before downstream processing or export.
Part of Infoveave Data Automation on the Unified Data Platform.
When to use Drop Columns
- Your data source returns extra system columns — audit timestamps, internal IDs, nested JSON blobs — that downstream steps do not need
- You need to remove personally identifiable information or sensitive data fields before passing the dataset to reporting, export, or a third-party tool
- You are reducing dataset width to improve processing speed and report readability
- You are preparing a clean export for end users who should not see internal or technical fields
When to avoid it
- You want to keep specific columns and discard everything else — Select Columns is more efficient for that (it lets you list what to keep rather than what to remove)
- You want to remove rows rather than columns — use the Filter activities for row-level removal
- You want to rename a column rather than delete it — Rename Columns handles that without removing data
Where it fits in your Infoveave automation
Drop Columns 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 Drop Columns
Real scenarios where this transformation saves hours of manual work.
Remove PII Before Analytics Export
A healthcare data team strips patient name, contact, and address columns from clinical datasets before sending records to the analytics layer. Drop Columns removes those fields automatically on every EHR export — reducing compliance risk and ensuring the analytics database never receives identifiable information that belongs only in the secured source system.
Clean Up Product Feed Before Catalog Load
A retail team imports product catalogs from vendor APIs that include internal system fields like vendor_internal_id, sync_timestamp, and stale metadata columns. Drop Columns removes those fields before the catalog data enters the master product database, keeping the target schema clean and preventing phantom columns from accumulating over time.
Strip Nested JSON Before Transaction Processing
A finance team processes transaction records exported from a payment gateway that embeds nested JSON objects for card metadata and raw request payloads alongside the flat fields needed for reconciliation. Drop Columns removes the embedded JSON columns before aggregation, eliminating parse errors and reducing the dataset to only the fields the reconciliation workflow actually uses.
See Drop Columns in action
Input data (left) is transformed using the configuration below. The output table (right) is ready for dashboards or downstream steps.
name, contact, name_first_name, name_last_nameInput Data
| Employee ID | name | age | department | title | salary | contact | skills | phone | full_name | |
|---|---|---|---|---|---|---|---|---|---|---|
| E001 | {first:John,last:Doe} | 32 | Sales | Sales Manager | 75000 | {email:[email protected]} | [CRM] | [email protected] | +1-555-1234 | John Doe |
| E002 | {first:Jane,last:Smith} | 28 | Engineering | Developer | 85000 | {email:[email protected]} | [Python] | [email protected] | +1-555-5678 | Jane Smith |
Output Data
| Employee ID | age | department | title | salary | skills | phone | full_name | |
|---|---|---|---|---|---|---|---|---|
| E001 | 32 | Sales | Sales Manager | 75000 | [CRM] | [email protected] | +1-555-1234 | John Doe |
| E002 | 28 | Engineering | Developer | 85000 | [Python] | [email protected] | +1-555-5678 | Jane Smith |
Configuration
Key fields to configure in the Infoveave workflow builder. Full reference available in the documentation.
Columns
Select one or more column names to remove from the dataset. Multiple columns can be dropped in a single step. All other columns pass through unchanged in their original order.
Multi-Column Drop
There is no limit on how many columns you can remove in one Drop Columns activity. Select as many columns as needed — system fields, nested objects, audit metadata, PII — and they are all removed in a single step. This avoids chaining multiple activities when you have many unwanted fields.
Frequently asked questions
Everything you need to know about Drop Columns in Infoveave.
Also in Column Operations — and what runs before & after
Transformations in the same family as Drop Columns, often chained together in the same Infoveave workflow.
Part of Infoveave Data Automation
80+ transformations. Zero manual steps.
Drop Columns 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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