Simplified Data Merge
Infoveave Data Automation — File Reading
Two datasets in. One unified table out. With or without exact matching.
Workflows that pull data from multiple sources — e-commerce orders and in-store transactions, production shifts from machines A and B, week-1 and week-2 expense reports — produce separate datasets that analysts normally merge manually. Simplified Data Merge combines any number of upstream activity outputs into one table in a single step, eliminating the copy-paste consolidation that used to happen at the end of every reporting cycle.
What Simplified Data Merge does
Combine the output from two or more upstream activities into a single unified dataset inside your Infoveave workflow. Merge all columns or only matching columns, with optional fuzzy matching for approximate-value consolidation.
When to use Simplified Data Merge
- Your workflow ingests data from multiple files, APIs, or systems in parallel branches that need to be combined before further transformation
- You need to stack rows from two datasets with the same column structure — the equivalent of SQL UNION ALL
- You have data from sources with slightly different values in key fields (typos, abbreviations) that need approximate matching before consolidation
- You want to combine outputs from separate Read CSV Files or Read Excel Files steps into one table for aggregation or reporting
When to avoid it
- You need a SQL-style JOIN (matching rows on a key column) — Simplified Data Merge stacks rows, not joins them by key
- Your two datasets have completely different schemas and no shared meaning — consider transforming each separately first
Where it fits in your Infoveave automation
Simplified Data Merge 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 Simplified Data Merge
Real scenarios where this transformation saves hours of manual work.
E-Commerce and In-Store Sales Consolidation
A retailer runs parallel pipeline branches — one ingesting e-commerce orders via API, one reading in-store POS CSV exports. Simplified Data Merge combines both into one unified sales table before aggregation and dashboard feeds.
Multi-Region Expense Report Stacking
Expense reports from three regional teams are ingested separately via file uploads. Simplified Data Merge stacks them into one unified dataset, enabling total spend analysis and budget vs actual reporting across all regions in one step.
Multi-Machine Production Log Merging
Production data from Machine A and Machine B is extracted separately from two different systems. Simplified Data Merge combines both logs into one dataset that feeds the daily OEE calculation pipeline.
See Simplified Data Merge in action
Input data (left) is transformed using the configuration below. The output table (right) is ready for dashboards or downstream steps.
Activity1, Activity2Merge all columnsfalseInput Data
| Category | Sales |
|---|---|
| Electronics | 10 |
| Home | 20 |
| Furniture | 20 |
Output Data
| Category | Sales |
|---|---|
| Electronics | 10 |
| Home | 20 |
| Furniture | 20 |
| Electronics | 30 |
| Home | 40 |
| Kitchen | 50 |
Configuration
Key fields to configure in the Infoveave workflow builder. Full reference available in the documentation.
Tables
Select two or more upstream activity outputs to merge. All selected tables are combined into one dataset. Activities can be run in parallel branches and merged here.
Merge Type
Merge all columns — includes every column from all sources, filling gaps with nulls where a column is absent from one source. Merge only common columns — retains only columns that appear in all selected sources, discarding source-specific columns.
Use Fuzzy Join
When enabled, rows are matched using approximate similarity rather than exact equality. Useful when values in the same field differ slightly across sources — for example, 'ABC Corp' vs 'ABC Corporation'.
Threshold
Only visible when Use Fuzzy Join is enabled. Controls the similarity score (0 to 1) required for a match. Higher values demand closer resemblance — typically 0.85 or above for most business data similarity scenarios.
Frequently asked questions
Everything you need to know about Simplified Data Merge in Infoveave.
Also in File Reading — and what runs before & after
Transformations in the same family as Simplified Data Merge, often chained together in the same Infoveave workflow.
Part of Infoveave Data Automation
80+ transformations. Zero manual steps.
Simplified Data Merge 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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