Concat Tables | Merge Datasets Across Rows or Columns - No Code Required

Merging data across sources is often a complex, error-prone task - especially when row counts, column types, or schema definitions don’t align. In most platforms, handling this requires SQL scripting or ETL logic.

Edilitics eliminates this friction with a governed, no-code Concat operation that supports multiple types of merging - across rows, columns, or both. With built-in schema validation and flexible duplicate handling, it’s designed for enterprise-grade data integration.

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Why Concat Operations Matter#

Combining datasets is central to most data workflows, but poor handling can lead to:

• Misaligned joins (due to column or row mismatches)

• Duplicate records that inflate aggregates

• Dropped data because of type incompatibility

With Edilitics, users can perform complex merges without writing code. Each concat operation is:

• Previewed in real-time

• Validated for compatibility

• Configurable for duplicate behavior

• Executed across integrated databases

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Concat Types Supported in Edilitics#

Type	Description	Validation Rules
Vertical Concat	Appends rows from one table to another	Columns in both tables must match in name and data type
Horizontal Concat	Adds columns from one table alongside another	Tables must have the same number of rows
Diagonal Concat	Merges both rows and columns for complex integrations	Both tables must match in row count, column count, and types

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How to Concat Tables in Edilitics#

• Select the operation type

  Choose Vertical, Horizontal, or Diagonal from the Concat options.

• Choose tables to merge

  Select both tables (from the same or different databases) to be concatenated.

• Configure duplicate rules

  Decide how duplicates should be handled:

  • Keep First – Retain the first instance of a record

  • Keep Last – Retain the last instance of a record

  • Drop All – Remove all instances of duplicates entirely

• Perform Checks and Adjustments

  Edilitics automatically ensures:

  • Compatibility Check – Verifies column types, count, and structure

  • Column Mismatch Notification – Flags mismatches and allows adjustments (e.g., drop, rename)

• Preview and Execute

  Review merged previews, validate results, and run the operation securely.

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Practical Use Cases for Concat#

Industry	Concat Type	Scenario
Retail	Vertical	Merge regional sales data for national-level reporting
Healthcare	Horizontal	Combine lab, admission, and prescription records into a single patient view
Finance	Vertical	Stack quarterly reports into an annual dataset
Manufacturing	Diagonal	Integrate production and supply logs for full pipeline visibility
Education	Vertical	Combine student results from multiple semesters

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Manual Equivalent – SQL & Pandas Examples#

Here’s how the same logic would be implemented manually.

SQL Example – Vertical Concat with Deduplication (Redshift)#

To simulate “Keep Last” or “Drop All”, you'd need row-level tracking with ROW_NUMBER() - something Edilitics handles via dropdown.

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Pandas Example – Horizontal Concat with Duplicate Handling#

Edilitics does all of this with real-time previews, dropdowns, and compatibility validation - no debugging required.

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Reliable, Governed Integration#

Every concat operation in Edilitics is designed for:

• Schema-aware validation before execution

• Clear duplicate control via dropdown

• Real-time previewing of final structure

• Cross-database support for federated datasets

By centralizing concat logic, Edilitics ensures your integrated data remains clean, consistent, and analysis-ready.

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Merging tables shouldn’t require guesswork or complex joins. With Concat, Edilitics simplifies multi-table integration through governed vertical, horizontal, and diagonal merges - complete with compatibility checks and duplicate handling. Whether you’re stacking records or combining attributes, data consolidation is now secure, flexible, and just a few clicks away.

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Next: Continue Structuring Your Dataset#

After merging your datasets with Concat, you may want to:

• Drop or Rename Columns to clean up the result

• Cast Data Types for compatibility with downstream tools

• Filter Rows to refine the integrated dataset

Each of these operations complements Concat, helping you clean, structure, and prepare your merged data for deeper analysis.