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Databricks January 2025 10 min read

Before You Buy Databricks: The Data Preparation Layer Nobody Talks About

Databricks is exceptional infrastructure for AI systems. But it assumes your data is already clean, structured, and semantically coherent. Here's what happens when that assumption fails - and what to do about it.

Databricks positions itself as a unified data and AI platform. The pitch is compelling: build RAG systems with Agent Framework, leverage Vector Search for retrieval, orchestrate with MLflow, evaluate with Mosaic AI. It's integrated, scalable, and production-ready.

The demos are impressive. During your proof-of-concept, the Databricks solutions architect shows you a working RAG system built in days. Documents get chunked, embedded, indexed, and retrieved with remarkable accuracy. The LLM responses are coherent and relevant. Your stakeholders are convinced.

Then you deploy with your actual data.

The pattern is predictable: 3-6 months after a Databricks deployment, we get called in to rescue RAG implementations that failed. Not because Databricks doesn't work - it works perfectly. But because the data feeding into it was never prepared for machine understanding.

What Databricks Actually Provides

Let's be precise about what you're buying:

Databricks excels at infrastructure:

  • Delta Lake for versioned data storage
  • Vector Search for similarity lookups
  • Agent Framework for RAG orchestration
  • MLflow for experiment tracking and model serving
  • Unity Catalog for governance
  • Mosaic AI for evaluation and monitoring

These are genuinely powerful capabilities. The platform provides everything you need to build, deploy, and scale AI systems - assuming your data is ready.

What Databricks doesn't provide:

  • Domain-specific data preparation expertise
  • Taxonomy standardization for your industry
  • Semantic cleaning of inconsistent codesets
  • Document chunking strategy for your use cases
  • Metadata enrichment that makes retrieval work

Databricks provides the tools. You still need the labor, domain expertise, and semantic work to make those tools useful.

Why Databricks RAG Demos Work and Production Deployments Fail

The demo uses carefully curated sample data. Documents have consistent formatting. Terminology is standardized. Metadata is complete and accurate. Chunks are semantically coherent. Of course it works.

Your production data looks different:

Inconsistent taxonomies: Department A uses "Type-1 equipment" while Department B calls the same thing "Category-A assets." Your documents span 15 years of terminology evolution with no formal specification. Databricks can index this - but retrieval accuracy plummets because semantically identical concepts have different labels.

Poor chunking: You chunk by token count or paragraph breaks because that's easiest. But your technical documents have tables that need to stay together, diagrams that provide critical context, and definitions that span multiple paragraphs. Your chunks break semantic boundaries, and your RAG system confidently provides incomplete or wrong answers.

Missing metadata: Half your documents have incomplete metadata. File names like "final_v2_ACTUAL_USE_THIS.pdf" don't help retrieval. The LLM can't determine document authority, recency, or relevance because that information doesn't exist in your index.

Domain-specific terminology: Your industry uses specialized terms that mean different things in different contexts. "Capacity" in energy infrastructure means something completely different from "capacity" in financial services. Generic embeddings capture surface similarity but miss semantic meaning.

The result: You've invested £500,000+ in Databricks, spent months on implementation, and your RAG system produces 40-60% accuracy instead of the 90%+ you saw in demos. Stakeholders are frustrated. The project gets labeled a failure. But Databricks itself works fine - your data was the problem.

The Data Preparation Work Databricks Can't Do

Here's what actually needs to happen before Databricks can help you:

1. Taxonomy Standardization

Your internal classification systems need formal specifications:

  • URIs for each codeset value (so "Type-1" and "Category-A" can be recognized as identical)
  • Version control (so you know which taxonomy version a document uses)
  • Cross-references between related codesets
  • Governance policies for taxonomy evolution

This typically takes 20-80 hours per codeset initially, with significant scaling advantages as your toolkit matures. Databricks provides Delta tables to store this - but it can't create the taxonomies themselves.

2. Document Corpus Preparation

Your documents need intelligent parsing and structuring:

  • Document type detection (technical spec vs. procedure vs. report)
  • Structure extraction (sections, tables, diagrams, references)
  • Relationship mapping (which documents cite which, what's authoritative)
  • Quality assessment (is this document complete? Current? Reliable?)

Databricks Autoloader can ingest documents. But determining how to parse a 50-page engineering specification with embedded diagrams requires domain expertise, not platform features.

3. Chunking Strategy

How you segment documents determines retrieval quality:

  • Semantic boundaries (don't split related content)
  • Context windows (how much context does each chunk need?)
  • Overlap strategy (how do chunks connect?)
  • Special handling (tables, lists, definitions, procedures)

This is highly domain-specific. Medical protocols need different chunking than legal contracts. Engineering specifications need different chunking than financial reports. There's no universal solution - and Databricks can't determine this for you.

4. Metadata Enrichment

Every chunk needs metadata that enables accurate retrieval:

  • Document authority (is this official? Draft? Superseded?)
  • Temporal information (when was this valid? Is it current?)
  • Domain classification (what area does this cover?)
  • Relationship metadata (how does this connect to other content?)

Much of this metadata doesn't exist in your source documents. It needs to be generated through domain expertise and semantic analysis. Databricks can store and index this metadata - once you create it.

5. Quality Validation

Before deploying, you need to know if your RAG system actually works:

  • Test query sets representative of real usage
  • Ground truth for expected retrieval results
  • Evaluation metrics beyond generic benchmarks
  • Iterative refinement based on failure analysis

Mosaic AI provides evaluation tools. But defining what "good" means for your use case requires understanding your domain, users, and requirements - not just running automated metrics.

The Economics of Data Preparation

Here's the uncomfortable math:

A typical Databricks implementation costs £500,000-£2,000,000+ including:

  • Platform licensing
  • Implementation consulting
  • Integration work
  • Training
  • Ongoing operational costs

Proper data preparation costs £60,000-£120,000 for most organizations:

  • 2-3 week assessment: £12,500
  • 8-12 week preparation project: £60,000-£100,000
  • Ongoing refinement: £10,000-£15,000/quarter

That's roughly 10-20% of your total Databricks investment - but it's the difference between success and failure.

Consider this: Would you spend £500,000 on infrastructure without ensuring your data can actually use it? Would you build a factory without checking whether your raw materials meet specifications?

Three Ways to Work Data Preparation into Your Databricks Project

Approach 1: Pre-Implementation (Recommended)

Do the data preparation work before deploying Databricks:

  1. Assess data readiness (2-3 weeks)
  2. Standardize taxonomies and clean data (8-12 weeks)
  3. Deploy Databricks with prepared data
  4. Iterate based on production results

This adds 10-15 weeks to your timeline but dramatically increases success probability. You deploy Databricks once, properly, with clean inputs.

Approach 2: Parallel Implementation

Run data preparation in parallel with Databricks deployment:

  • Your implementation consultants build the platform
  • Data preparation team cleans and structures the data
  • Both work streams converge at production deployment

This doesn't extend your timeline but requires coordination between teams. Your Databricks consultants handle infrastructure; data preparation specialists handle semantic work.

Approach 3: Rescue Projects (Most Common, Most Expensive)

This is what we see most often: organizations deploy Databricks, discover their RAG systems don't work, and then call us to fix the data layer. This is the most expensive approach because:

  • You've already spent the full Databricks implementation budget
  • Stakeholder confidence is damaged
  • Timeline pressure is intense
  • Retrofitting data fixes is harder than doing it right initially

If you're reading this after deployment, you're not alone - this is exactly when most organizations realize data preparation matters.

Working Inside Your Databricks Environment

One advantage: data preparation work integrates seamlessly with Databricks:

  • Prepared data loads into Delta tables
  • Standardized taxonomies live in Unity Catalog
  • Quality metrics integrate with Mosaic AI
  • Chunking strategies parameterize in notebooks

We don't replace Databricks - we ensure it has the clean inputs it needs to function. Your Databricks investment still provides all the infrastructure value, but now it actually works with your data.

What Success Actually Looks Like

When you combine Databricks infrastructure with proper data preparation:

  • RAG retrieval accuracy matches demo performance (85-95%)
  • Hallucinations drop to acceptable levels
  • Users trust the system's responses
  • Stakeholders see ROI on the platform investment
  • The project gets labeled a success and expands

This isn't theoretical. Organizations that invest in data preparation before or during Databricks deployment see dramatically higher success rates. The platform works - when you give it data that's actually ready.

Databricks Data Readiness Assessment

Before you deploy (or while you're rescuing a failed deployment), let's assess whether your data is actually ready for Databricks. 2-3 week engagement, £12,500, tells you exactly what preparation work is required.

Schedule Assessment

The Bottom Line

Databricks is excellent infrastructure. It provides world-class tools for building production AI systems. But infrastructure without clean data is like a Ferrari with contaminated fuel - the engineering is impeccable, but it still won't run.

If you're evaluating Databricks, factor data preparation into your budget and timeline. It's not an optional nice-to-have - it's the foundation that determines whether your £500,000+ investment succeeds or fails.

And if you've already deployed and are struggling with accuracy, hallucinations, or stakeholder confidence: you're not alone, this is fixable, and doing the data preparation work now will salvage your investment.

Related reading: See our platform comparison analysis for how these challenges apply to Snowflake, BigQuery, and Microsoft Fabric as well.