Table of Contents

• Can It Handle My Volume?
• Try It Yourself — In Minutes
  • Step 1: Start a Database Starter Pack
  • Step 2: Seed Your Data Volume
  • Step 3: Generate Your Reports
  • Step 4: See Your Results
  • Step 5: Wipe and Try Again
• Supported Databases
• Tips for Large Volumes

Can It Handle My Volume?

DataPallas was built from the ground up to generate and distribute large numbers of reports. If you are evaluating it for payslips, invoices, statements, letters, or any other document type at scale, you are probably asking yourself: "Can it actually handle my volume?"

That is the right question. And the honest answer is: it depends. It depends on your hardware, your database, your report complexity, your output format, your server load. Every environment is different, and no vendor benchmark can tell you what will happen on your machine with your data.

So instead of publishing numbers that may not reflect your reality, we built something better: a way for you to answer that question yourself, in minutes.

DataPallas includes a built-in data seeding tool inside its Starter Packs. You pick a database, seed it with the volume of data you expect in production — 1,000 records, 100,000, or a million — configure your reports, click Generate, and see exactly what you get. On your hardware. With your setup. No guesswork.

Try It Yourself — In Minutes

For a complete, hands-on walkthrough — from seeding data to configuring the datasource and template to generating 10,000 invoices see

Large-Scale Report Generation with MySQL

Note: Below you will see code examples — SQL queries, templates, report configurations. You don't have to write any of this yourself. DataPallas includes AI-powered tools that generate all the code for you. Click "Hey AI, Help Me with..." in the UI and tell it what you want.

Step 1: Start a Database Starter Pack

Go to Help & Support → Apps / Starter Packs / Extra Utils and pick the database vendor you want to test with. Click Start — a Docker container with a sample database spins up automatically.

Note: You need Docker installed and running on your machine. Each starter pack launches a fully configured database container with realistic sample data preloaded.

Step 2: Seed Your Data Volume

Once the starter pack is running, the seed controls appear below the Start/Stop button:

1. Enter the number of records you want (e.g., 1,000 or 10,000 or 100,000 or 1,000,000).
2. Click Seed.
3. Wait for the seeding to complete — realistic data (customers, products, invoice headers, and line items) is generated into dedicated tables.

The seeding tool generates invoices because invoices are sufficiently complex — each one joins customer details, product information, and multiple line items. This is representative of any real-world business report, not a trivial single-table query. The performance you see with these invoices on your hardware, at your data volume, will be meaningful for any report type you generate in production — payslips, statements, work orders, or anything else.

The seeded data sits alongside the original sample data and does not interfere with it.

Step 3: Generate Your Reports

This is the moment of truth — not a number on a marketing page, but a real run on your real machine.

Step 4: See Your Results

Step 5: Wipe and Try Again

Click Wipe out the Seeded Data to remove all previously seeded invoice data.

Then seed a different volume and test again. Try 10x more. Try a different database engine. Try a different output format. Compare.

Supported Databases

The data seeding tool works with all database starter packs:

• Oracle
• SQL Server
• PostgreSQL
• MySQL
• MariaDB
• IBM Db2
• Supabase (PostgreSQL BaaS)

All seven vendors support the same workflow. The seeding engine handles vendor-specific differences internally — you just enter a number and click Seed.

Tips for Large Volumes

• Start small. Seed 1,000 records first to verify everything works, then scale up to 10K, 100K, or beyond.
• Monitor disk space. Generating a large number of documents takes disk space. Make sure your output drive has room.
• Windows Explorer and large folders. Explorer slows down when a single folder contains more than ~100,000 files. The generation itself is not affected — only the file browser struggles. Use the command line or archive tools to browse very large output folders.
• Try different databases. The same data volume can perform differently across database engines. Seed the same amount on multiple vendors and compare.
• Wipe before re-seeding. Always wipe the previous seeded data before seeding again to keep your test clean.

Configure DB Connections

 

Report Generation (Overview)