Platform Wars Don't Matter: Why the Databricks vs. Snowflake vs. Fabric Debate Misses the Point

Every quarter, the same debate rages in enterprise data circles: Databricks or Snowflake? Microsoft Fabric or Google BigQuery? Should we go all-in on one platform or stay multi-cloud?

These are billion-dollar decisions that consume months of evaluation cycles, involve multiple proof-of-concepts, and generate extensive RFPs. Meanwhile, a more fundamental question gets ignored: Does your data actually work with any of these platforms?

The Infrastructure Illusion

Here's what Databricks, Snowflake, Microsoft Fabric, and Google BigQuery all have in common: they're exceptional infrastructure. They provide world-class tools for orchestration, vector search, model serving, and evaluation. They can handle petabyte-scale data processing. They offer seamless integrations with every major AI framework.

Here's what else they have in common: they all assume your data is already clean, structured, and semantically coherent.

When your RAG system hallucinates on Databricks, the problem isn't Databricks. When your Snowflake Cortex retrieval accuracy sits at 40%, the problem isn't Snowflake. When your Microsoft Fabric AI fails to understand domain-specific terminology, the problem isn't Fabric.

The problem is that you're asking world-class infrastructure to process data that was never prepared for machine understanding.

What Every Platform Actually Provides

Let's be clear about what you're buying when you invest £500,000+ in any of these platforms:

Capability	Databricks	Snowflake	BigQuery	Fabric
Vector Search Infrastructure	✓	✓	✓	✓
Model Orchestration	✓	✓	✓	✓
Scalable Compute	✓	✓	✓	✓
Data Governance	✓	✓	✓	✓
Domain-Specific Data Preparation	✗	✗	✗	✗
Taxonomy Standardization	✗	✗	✗	✗
Semantic Data Cleaning	✗	✗	✗	✗
Document Chunking Strategy	✗	✗	✗	✗

Notice a pattern? These platforms provide the tools. They don't provide the labor, domain expertise, or semantic work required to make those tools actually useful.

Why Platform Demos Work and Production Deployments Fail

During a platform evaluation, the vendor demonstrates their capabilities using clean, well-structured sample data. The vector search works beautifully. The retrieval is accurate. The LLM responses are coherent. Everyone is impressed.

Then you deploy with your actual data:

• Asset classifications that evolved organically over 15 years with no formal specification
• Documents where department A calls something "Type-1" and department B calls the same thing "Category-A"
• PDFs with inconsistent formatting, embedded tables, and scanned images mixed with text
• Technical terminology that means different things in different contexts
• Metadata fields that are sometimes populated, sometimes not, and sometimes wrong

Suddenly, your £500,000 platform investment produces:

• RAG systems with 40-60% retrieval accuracy (vs. the 90%+ in demos)
• Hallucinations caused by semantically inconsistent chunks
• Search results that return irrelevant documents
• AI assistants that confidently provide wrong answers

The platform works perfectly. Your data doesn't.

The Real Question Isn't Which Platform - It's Whether Your Data Is Ready

Before you choose between Databricks, Snowflake, Fabric, or BigQuery, ask yourself:

1. Do you have formal specifications for your internal classification systems? If your codesets exist only in people's heads or undocumented Excel files, no platform can fix that.
2. Can you explain your chunking strategy? How are you segmenting documents? What metadata accompanies each chunk? How do you handle tables, lists, and technical diagrams?
3. Have you standardized your taxonomies? Do URIs exist for your classifications? Is there version control? Can different systems interoperate?
4. Do you have quality validation processes? How do you measure whether your RAG system is retrieving the right information? What's your testing framework?

If you answered "no" or "we're figuring that out" to any of these questions, you have a data preparation problem, not a platform selection problem.

What Actually De-Risks Your Platform Investment

Here's a more productive approach:

Step 1: Assess your data readiness
Before evaluating platforms, evaluate your data. What's the current state of your taxonomies? How clean are your documents? What metadata exists? This assessment typically costs around £12,500 and takes 2-3 weeks - a fraction of your platform investment.

Step 2: Prepare your data properly
This is the unglamorous work that actually makes AI systems function:

• Formalizing informal classification systems with URIs, versioning, and specifications
• Standardizing taxonomies across systems and departments
• Developing chunking strategies appropriate to your document types and use cases
• Enriching metadata so retrieval actually works
• Building quality validation frameworks

For most organizations, this costs £60,000-£120,000 and takes 8-12 weeks. That's roughly 20% of your platform investment - but it determines whether that investment succeeds or fails.

Step 3: Then choose your platform
Once your data is actually ready, the platform choice becomes much simpler. They all work when you feed them clean inputs. The decision comes down to ecosystem preferences, existing vendor relationships, and specific technical requirements - not fundamental capability differences.

The Infrastructure Vendors Won't Tell You This

Platform vendors have every incentive to focus on their infrastructure capabilities. They want you comparing their vector search performance, their model serving latency, their pricing tiers. That's what they're selling.

What they won't tell you is that their platforms provide the tools but not the domain expertise. They can't standardize your energy sector asset taxonomies. They don't understand your financial services risk classifications. They won't formalize your manufacturing supply chain codesets.

That work requires human expertise - domain knowledge combined with data engineering skill and semantic understanding. No platform, regardless of how sophisticated, can automate that away.

A Different Conversation

Instead of asking "Databricks or Snowflake?", ask:

• "Is our data prepared for any AI platform?"
• "Have we formalized our internal taxonomies?"
• "Do we have a chunking strategy that matches our use cases?"
• "Can we measure our retrieval accuracy before deployment?"

These questions matter more than which vendor's logo appears on your architecture diagram.

Platform-Specific Deep Dives

If you're already committed to a specific platform and want to understand the data preparation requirements in detail:

• Before You Buy Databricks: The Data Preparation Layer Nobody Talks About
• Refining the Fuel: Making Snowflake Cortex AI-Ready
• Beyond the Infrastructure: Why BigQuery RAG Needs Human-in-the-Loop Prep

Each post examines what that specific platform provides, what it doesn't, and exactly what data preparation work is required to make your investment successful.

The next time someone asks whether you should choose Databricks, Snowflake, Fabric, or BigQuery, the right answer might be: "Let's make sure our data is ready for any of them first."