Why Python is Your Secret Weapon for SAP Data Problems

The $180,000 Question Nobody Was Asking

Last month, a manufacturing company called us with what they thought was a simple question:

"We're moving to SAP S/4HANA. Can you help us figure out how much data we need to migrate?"

Seems straightforward, right? But here's what nobody had told them: SAP's new system charges based on how much data you store. It's like switching from unlimited cloud storage to paying per gigabyte — except we're talking about decades of business documents, invoices, contracts, and files.

We ran a Python script. One afternoon. Here's what we found:

• 3.2 million files in their system
• 1.3 million were duplicates or links to documents that no longer existed
• They were about to pay $180,000 per year to store data they couldn't even access

One script. One clear number. One very uncomfortable board meeting.

The Problem SAP Doesn't Talk About

SAP is an incredibly powerful system. It runs factories, manages supply chains, handles billions in transactions. But it was built in an era when storage was cheap and "just keep everything" was the strategy.

Now, three things have changed:

PythonMate solves this specific, high-urgency problem with Python-first engineering.

Why SAP Can't Answer Its Own Questions

Here's the thing most people don't realize: SAP wasn't designed to analyze itself.

SAP is brilliant at running your business. But ask it questions like:

• •"Show me all documents over 5 years old"
• •"Which files are broken or corrupted?"
• •"How much storage could we save by cleaning up duplicates?"
• •"Which documents are actually being accessed vs. just sitting there?"

...and SAP struggles.

It's like asking a filing cabinet to tell you which folders you haven't opened in 10 years. The filing cabinet doesn't know. It just stores things.

What Python Does That SAP Can't

Think of Python as a conversation translator.

SAP speaks its own language (ABAP, database queries, complex transactions). Python speaks... well, Python. Simple, clear, powerful.

# This simple Python script can scan millions of records
# and tell you exactly what you have

def check_documents_over_years_old(years=5):
    query = f"SELECT COUNT(*) FROM SOFFCONT1 WHERE RELID='LG' AND OBJID IN (
        SELECT OBJID FROM SADUMP WHERE TIMESTAMP < DATE_SUB(NOW(), INTERVAL {years} YEAR)
    )"
    result = execute_query(query)
    return result

def find_broken_links():
    query = "SELECT COUNT(*) FROM SRGBTBREL WHERE RELID='AN' AND OBJID NOT IN (
        SELECT OBJID FROM SOFFCONT1 WHERE RELID='AN'
    )"
    result = execute_query(query)
    return result

# Python runs on external server with unlimited RAM
from pyrfc import Connection
from multiprocessing.pool import Pool

conn = Connection(dest='SAP_SYSTEM')
# Process TB-scale data without memory limits
with Pool(processes=15) as pool:
    results = pool.map(process_batch, large_dataset)
# External processing, unlimited memory, parallel streaming

The "Health Check" Approach: See Before You Buy

Here's how this works in practice.

Real-World Example: The Invoice That Disappeared

A logistics company was preparing for an audit. They needed to show 5 years of customs invoices. SAP said: "Yes, you have 47,823 invoices in the system."

The auditor asked to see invoice #INV-2019-04782.

SAP found the database record. But the PDF attachment? Gone. The link was broken.

Panic.

They asked us: "Is this the only one?" We ran a Python script to check all 47,823 invoices. Result: 4,127 invoices had missing or corrupted attachments.

That's not a single mistake. That's a systematic problem that could have cost them millions in penalties. We fixed it in 3 weeks using Python to:

• Find all the broken links
• Locate the files in backup systems
• Re-attach them correctly
• Verify every single checksum matched

The audit passed. The CFO slept better.

Why Python? (Your Technical Moat)

The technical advantages that separate Python from ABAP for TB-scale processing:

PythonMate's Philosophy: Simple Tools, Big Impact

We believe in three things:

Which Clients? (ECC vs S/4HANA)

The "Bleeding Neck" market: ECC clients preparing for S/4HANA migration

Getting Started: The No-Risk First Step

The Bottom Line

Python isn't magic. SAP isn't broken.

But when you combine Python's simplicity with SAP's complexity, you unlock insights that most companies never see:

• Where your money is going
• What your risks actually are
• How much you can save with a little cleanup

And in an era where storage costs real money and auditors ask hard questions, those insights are worth their weight in gold.