Data Merging and Joining Guide

1. Introduction

Combining data from different sources is a fundamental step in reporting and analytics. Whether you are appending monthly sales reports or joining customer profiles with their transaction history, manual copy-pasting is inefficient and prone to formatting errors.

The SHRTX Data Merger provides an automated engine to perform vertical unions and horizontal joins on tabular files. It executes all operations locally in your browser, ensuring your business intelligence data remains secure.

2. Before You Start

Working with sensitive enterprise data requires strict local-first execution.

Technical Capabilities

• RFC-4180 Compliance: Safely parses complex nested quotes and line breaks during the merge.
• Format Flexibility: Supports CSV and TSV formats.
• Dynamic Padding: Automatically inserts blank cells when appending files with different column schemas.

3. Step-by-Step Guide

Follow this sequential pipeline to combine your tabular datasets securely.

Step 1. Ingest Your Files

Select your Primary File and Secondary File. The engine reads both streams into local memory buffers and auto-detects their delimiters and headers.

Step 2. Select Merge Strategy

Decide how the datasets relate to each other:

• Vertical Union (Append Rows): The secondary file’s rows are placed directly underneath the primary file’s rows. The engine maps column headers intelligently.
• Horizontal Join (Match Columns): The files are placed side-by-side. A shared “Key Column” (e.g., “User ID”) is used to line up the correct rows.

Step 3. Configure the Merge

Depending on your strategy, configure the final parameters.

For Vertical Unions: The engine automatically handles schema mapping. If File A has columns [Name, Email] and File B has [Name, Phone], the result will be [Name, Email, Phone], with blanks inserted where data is missing.

For Horizontal Joins:

• Select the Key Column: Choose the column header that exists in both files to link them together.
• Choose Join Type:
  • Left Join: Keeps every row from the Primary File. If a matching row is found in the Secondary File, its data is appended. If not, the new columns are left blank.
  • Inner Join: Keeps only the rows that exist in both files. If a User ID is in the primary file but missing from the secondary file, that entire row is dropped.

Step 4. Export the Merged File

Review the visual preview table to confirm the merge was successful. Download the output file back to your machine.

4. Understanding Results

The visual feedback panel helps you verify the integrity of the merge.

• Primary File Stats: The row count of your base file.
• Secondary File Stats: The row count of your appended file.
• Final Output Rows: The total number of rows in the downloaded file, which verifies if an Inner Join dropped rows or a Union added them.

5. Practical Examples

Appending Monthly Reports (Union)

• Situation: You have January and February sales CSVs with slightly different column orders.
• Action: Select Vertical Union.
• Result: A single Q1 report with all rows combined and headers perfectly aligned.

Enriching Customer Data (Left Join)

• Situation: A master list has emails, and a secondary list has phone numbers mapped to those emails.
• Action: Select Horizontal Join (Left Join) using “Email” as the key.
• Result: The master list now contains a new “Phone” column without losing any original contacts.

6. Common Mistakes

• Mismatched Key Column Names: For horizontal joins, the primary key column name must be exactly identical in both files. If one file uses “User_ID” and the other uses “id”, you must use a tool like the CSV Cleaner to rename them first.
• Duplicate Keys in Joins: If your primary file has duplicate keys (e.g., two rows for the same User ID), a horizontal join can create unexpected Cartesian products. Deduplicate your files before joining them horizontally.

7. Troubleshooting

• Empty Join Results: If an Inner Join results in zero rows, it means the data in the key columns did not match exactly. Check for trailing spaces or casing differences.
• File Freezing: If joining files with millions of rows causes browser stutter, close unnecessary tabs to free up system RAM.

8. Privacy & Browser-Native Execution

Your tabular data never leaves your device. All parsing, schema mapping, and join logic executes strictly within your local browser sandbox.

SHRTX is a tool provider, not a data processor. Your session state is transient and resides only in your device RAM. Closing the browser tab immediately purges the datasets from memory.