Prabakar's GitHub Tools

A lightweight, browser-based toolbox for everyday data engineering, data analysis, and developer tasks. It is a static GitHub Pages site with no backend, accounts, or dependencies.

Live Site

Open the toolkit at prabakaras.github.io/dataengineeringtools.

Why This Exists

Small data tasks often interrupt delivery work: formatting a payload, checking a CSV extract, comparing a SQL query, or creating safe records for a test environment. This project keeps those utilities in one public, fast-loading workspace. Every tool runs in the browser so it can be used without installing software or sharing working data with a service.

Available Tools

Tool	Purpose	Link
JSON Formatter & Validator	Validate and format JSON with two-space indentation.	Open tool
CSV / JSON Converter	Convert CSV to JSON or an array of JSON objects to CSV.	Open tool
SQL Formatter	Structure SQL queries and choose uppercase or lowercase keywords.	Open tool
CSV Data Profiler	Review rows, nulls, uniqueness, inferred types, and samples.	Open tool
Text Diff	Compare two text blocks line by line.	Open tool
Timestamp Converter	Convert epoch values, ISO dates, UTC, and local time.	Open tool
Regex Tester	Test JavaScript regular expressions and inspect matches.	Open tool
YAML Validator	Check common YAML indentation and mapping issues.	Open tool
Data Quality Rules	Generate starter SQL for null, duplicate, range, and value checks.	Open tool
Data Masking	Mask common sensitive values before sharing text extracts.	Open tool
Cron Helper	Explain standard five-field cron expressions.	Open tool
Synthetic Data Generator	Generate test data from common DDL and export CSV, Excel-compatible data, or SQL inserts.	Open tool
JSON to Table	Flatten JSON records into a searchable table and export the results.	Open tool
PII Scanner	Detect common sensitive values and create a redacted copy locally.	Open tool
JSON Schema Generator	Infer a draft 2020-12 JSON Schema from example JSON.	Open tool
Schema Diff	Compare JSON schemas and report contract changes by property path.	Open tool
SQL Data Explorer	Import CSV, JSON, or Parquet and run local SQL queries using DuckDB-Wasm.	Open tool
Parquet Viewer	Inspect Parquet schema and preview rows locally in the browser.	Open tool
Tool Usage Analytics	View, export, and clear local page usage metrics on this device.	Open tool

Common Workflows

Clean and inspect data

1. Use the JSON Formatter to validate payloads or API responses.
2. Use the CSV / JSON Converter when moving tabular data between tools.
3. Use the CSV Data Profiler to check row count, populated values, unique values, inferred types, and samples.
4. Use Data Masking before pasting an extract into tickets, documentation, or chat.

Prepare queries and checks

1. Format a query with the SQL Formatter before review or handoff.
2. Generate a starter null, duplicate, range, or accepted-value check with Data Quality Rules.
3. Compare versions of a query or configuration file with Text Diff.
4. Use Cron Helper to explain a standard five-field schedule.

Create safe test records

1. Open the Synthetic Data Generator.
2. Paste a common CREATE TABLE statement and select the number of rows, from 1 to 10,000.
3. Generate records and review the in-browser preview.
4. Export the generated data in CSV, Excel-compatible .xls, or SQL INSERT format.

The generator recognizes common type and column-name patterns for IDs, names, email addresses, city, status, phone numbers, UUIDs, dates, numbers, and booleans. It supports conventional comma-separated column definitions. Table-level constraints, advanced dialect syntax, computed columns, and nonstandard DDL should be reviewed or simplified before generation.

Scan before sharing

1. Open the PII Scanner.
2. Paste a log, payload, extract, or document.
3. Review detected email addresses, phone numbers, IP addresses, UUIDs, card-like numbers, and token-like values.
4. Copy the redacted result after reviewing it manually.

The scanner uses browser-side pattern matching. It is a review aid, not a complete privacy or secrets-detection system.

Explore local files with SQL

1. Open SQL Data Explorer.
2. Import a local CSV, JSON, or Parquet file.
3. Run SQL queries over the in-browser view.
4. Review result tables without uploading data.

DuckDB-Wasm is downloaded by the browser when the tool is used. Query execution remains local.

Inspect Parquet files

1. Open Parquet Viewer.
2. Import a .parquet file.
3. Review inferred schema details and row previews.

Large files may use noticeable memory because all processing stays in the browser.

Local usage analytics

The Tool Usage Analytics page shows local per-page view counts stored in localStorage on your device.

• Metrics are not uploaded by default.
• You can export usage JSON from the analytics page.
• You can clear local usage data at any time.

Exports

The Synthetic Data Generator provides three local export options:

Export	Use case
CSV	Load sample data into spreadsheets, BI tools, or data platforms.
Excel-compatible .xls	Open the generated table directly in Microsoft Excel.
SQL INSERT script	Load fictional records into a SQL Server-style test table.

Generated values are fictional. Review output before running it in a database or sharing it outside your team.

Privacy

All processing happens locally in the browser. Pasted content, imported files, generated records, and exported files are not uploaded to a server.

This is a client-side convenience tool, not a replacement for approved data-protection controls. Do not paste production secrets, access tokens, customer data, or regulated data unless your organization permits local browser processing.

Browser Support

Use a current version of Chrome, Edge, Firefox, or Safari. File download and clipboard behavior can vary based on browser and local security settings.

Run Locally

Clone the repository and open index.html directly in a browser. No build process, package installation, or server is required.

git clone https://github.com/prabakaras/dataengineeringtools.git
Set-Location dataengineeringtools
Start-Process index.html

The shared behavior lives in app.js, and site-wide styling lives in styles.css. Each utility is a standalone HTML page, which keeps the site compatible with GitHub Pages.

Contributing

Contributions should preserve the core design constraints:

• Keep processing local to the browser; do not add telemetry, credentials, or a backend for routine tools.
• Use accessible labels, keyboard-friendly native controls, and clear validation feedback.
• Keep new tools dependency-free when practical and add them to the landing page and this README.
• Validate JavaScript with node --check app.js before submitting a change.
• Do not include real customer, production, or secret data in examples or commits.

Publish Updates

GitHub Pages deploys the contents of the main branch from the repository root. Push changes to main to publish an update. The live URL is https://prabakaras.github.io/dataengineeringtools/.

Discovery

The landing page includes search and social-sharing metadata. robots.txt and sitemap.xml allow search engines to discover the landing page and each tool route. Submit https://prabakaras.github.io/dataengineeringtools/sitemap.xml to Google Search Console and Bing Webmaster Tools after verifying ownership of the GitHub Pages property.