The Uncomfortable Truth About Your Data Platform
Here's a question every business leader should be asking their IT team right now:
"Why does our data platform cost this much, take this long to deliver insights, and require this many people to maintain?"
If the answer involves phrases like "we're still building out the lakehouse", "the Spark clusters need tuning", or "we're waiting on the platform team to provision environments" — you've fallen into the enterprise data platform trap.
There's a simpler path. One that delivers better performance, lower costs, and fewer headaches. And most organisations aren't even evaluating it.
The Modern Data Stack Has Become Absurdly Complex
Let's be honest about what "enterprise data platforms" have become.
Databricks started as managed Spark. Now it's notebooks, Unity Catalog, Delta Live Tables, MLflow, Feature Store, SQL Warehouses, and a pricing model that requires a spreadsheet to understand.
Snowflake was beautifully simple — until it wasn't. Now you're navigating Snowpark, Streamlit, Cortex AI, Dynamic Tables, and consumption-based pricing that surprises finance every quarter.
Microsoft Fabric took everything in the Microsoft data ecosystem and put it in a trench coat pretending to be one product. Power BI, Synapse, Data Factory, Real-Time Analytics, and a capacity-based licensing model that nobody fully understands.
Each of these platforms has merit. Each solves real problems. But each has also accumulated complexity that most organisations simply don't need.
The result? Expensive implementations that underperform, require specialised talent to maintain, and deliver questionable ROI.
The Alternative: Elegant Simplicity
Consider a different architecture:
| Component | Purpose |
|---|---|
| ClickHouse Cloud | Analytical database — storage, compute, and query engine |
| Power BI | Business intelligence and visualisation |
| Airbyte | Data ingestion and pipeline automation |
| LibreChat + ClickHouse MCP | AI-powered natural language analytics |
That's it. Four components. Each does one thing exceptionally well.
Let me explain why this stack deserves serious evaluation.
Why ClickHouse Changes the Economics
ClickHouse wasn't adapted for analytics — it was built for analytics from the ground up.
Raw Performance
ClickHouse uses columnar storage, vectorised query execution, and aggressive compression. The practical result is that queries which take minutes in traditional data warehouses often complete in seconds.
This isn't marketing fluff. The ClickBench benchmarks consistently show ClickHouse outperforming alternatives by significant margins on analytical workloads.
Cost Structure
ClickHouse Cloud separates storage and compute, but without the abstraction layers that inflate costs elsewhere. You're paying for:
- Storage (compressed, so less than you'd expect)
- Compute (scales to zero when idle)
- That's essentially it
Compare this to Databricks DBUs, Snowflake credits, or Fabric capacity units — pricing models designed to be difficult to predict and optimise.
Operational Simplicity
ClickHouse Cloud is genuinely managed. You don't need a platform team to configure clusters, tune Spark executors, or manage infrastructure. The service handles scaling, backups, and maintenance.
This matters enormously for organisations without dedicated data platform engineers.
Materialised Views: Forced Architectural Discipline
Here's something most ClickHouse evaluations overlook: materialised views fundamentally change how teams build data pipelines.
In ClickHouse, materialised views:
- Execute transformations at ingestion time
- Enforce explicit contracts between data layers
- Create clear lineage from raw to refined data
- Cannot easily be bypassed or bodged
This naturally implements the medallion architecture (bronze → silver → gold) without requiring process discipline or governance overhead.
Compare this to a typical Databricks environment where:
- 47 different notebooks implement transformations
- Each data engineer has their own approach
- Lineage is scattered across jobs and workflows
- Nobody's quite sure which version of the data is "correct"
ClickHouse's materialised views don't just improve performance — they impose structure. For organisations without mature data engineering practices, this constraint is a feature, not a limitation.
The "Software People Aren't Data People" Problem
Let's address the elephant in the room.
Modern data platforms assume you have:
- Platform engineers who understand Kubernetes and cloud infrastructure
- Data engineers who understand Spark internals and distributed computing
- Analytics engineers who understand dbt, semantic layers, and transformation patterns
- BI developers who understand data modelling and visualisation
Most organisations don't have these specialists. They have generalised IT teams who are expected to do everything.
The ClickHouse + Power BI stack respects this reality:
ClickHouse speaks SQL. Not Spark SQL with its quirks. Not proprietary query languages. Standard SQL that any database-literate person can write.
Power BI is familiar. Business users already know it. The learning curve for analysts is minimal.
Airbyte provides pre-built connectors. You're not writing custom ingestion code. You're configuring established connectors.
Administration is minimal. ClickHouse Cloud handles the infrastructure. There's no cluster management, no executor tuning, no garbage collection optimisation.
This isn't about dumbing things down. It's about choosing tools that match your organisation's actual capabilities.
The AI Analyst That Actually Works
Here's where things get interesting for 2026 and beyond.
ClickHouse has released an MCP (Model Context Protocol) integration. Combined with LibreChat or similar interfaces, you get an AI analyst that can:
- Query your data directly using natural language
- Understand your schema and relationships
- Generate and execute SQL against ClickHouse
- Return results and visualisations
This isn't a bolt-on feature that requires a separate AI platform, vector database, and orchestration layer. It's a direct integration between your analytical database and AI capabilities.
The queries are fast because ClickHouse is fast. The results are accurate because the AI is querying real data, not summarised embeddings. The scaling is handled because both components are cloud-native.
Compare this to implementing AI analytics on Databricks (requires Mosaic AI, model serving, and significant configuration) or Snowflake (requires Cortex, which is still maturing and adds cost complexity).
What About Governance and Compliance?
This is where I'll be direct about limitations.
ClickHouse Cloud's governance capabilities are less mature than Unity Catalog or Microsoft Purview. If you're in a heavily regulated industry with complex compliance requirements — healthcare, financial services, government — you'll need to evaluate whether ClickHouse's current governance features meet your needs.
That said, most organisations' governance requirements are simpler than they believe. Role-based access control, audit logging, and data encryption cover the majority of use cases. ClickHouse Cloud provides these.
The question to ask is: "Do we need enterprise governance features, or have we been told we need them?"
Many organisations implement complex governance frameworks because vendors sold them on the requirement, not because regulations actually demanded it.
The Total Cost Comparison
Let's talk numbers — not hypothetical benchmarks, but the real costs organisations experience.
Databricks
- Compute costs scale with DBU consumption, which is difficult to predict
- Storage costs for Delta tables
- Premium features (Unity Catalog, Model Serving) add additional DBU multipliers
- Typically requires dedicated platform engineering resources
- Common complaint: costs 2-3x initial estimates after first year
Snowflake
- Credit consumption varies dramatically based on query patterns
- Virtual warehouse sizing requires ongoing optimisation
- Premium features (Snowpark, Cortex) add cost complexity
- Common complaint: finance teams consistently surprised by monthly invoices
Microsoft Fabric
- Capacity-based pricing requires right-sizing that's difficult to predict
- Unused capacity is wasted spend
- Feature availability varies by capacity tier
- Common complaint: nobody understands how to optimise capacity utilisation
ClickHouse Cloud + Power BI + Airbyte
- ClickHouse Cloud: predictable compute + storage costs, scales to zero
- Power BI: per-user licensing (Pro) or capacity (Premium) — well understood
- Airbyte Cloud: usage-based pricing on data volume — straightforward
The total is typically 40-60% lower than equivalent enterprise platform implementations, with significantly less variance in monthly costs.
The Implementation Reality
Let's compare what "getting started" actually looks like.
Enterprise Platform (Fabric/Databricks/Snowflake)
- Procurement and licensing negotiation (2-4 weeks)
- Environment provisioning and configuration (2-4 weeks)
- Network and security integration (2-4 weeks)
- Platform team onboarding and training (4-8 weeks)
- Initial pipeline development (4-8 weeks)
- First production workload (4-6 months total)
ClickHouse Cloud + Power BI
- Sign up for ClickHouse Cloud (1 day)
- Configure Airbyte connections to source systems (1-2 weeks)
- Create initial tables and materialised views (1-2 weeks)
- Connect Power BI (1 day)
- First production workload (3-4 weeks total)
This isn't because ClickHouse is less capable. It's because the architecture has fewer moving parts, fewer integration points, and less configuration surface area.
Time-to-value is dramatically shorter.
When NOT to Choose This Stack
I'm not arguing this stack is universally superior. There are legitimate reasons to choose alternatives:
Choose Databricks if:
- Machine learning and feature engineering are core to your use case
- You need tight integration between data engineering and ML workflows
- You have a mature platform engineering team
Choose Snowflake if:
- You need extensive data sharing capabilities across organisations
- Your workload involves complex multi-table joins as the primary pattern
- You're heavily invested in the Snowflake partner ecosystem
Choose Fabric if:
- You're deeply committed to the Microsoft ecosystem
- Your organisation mandates Microsoft tooling
- You need tight integration with Microsoft 365 and Azure services
But evaluate ClickHouse + Power BI if:
- Your primary use case is analytical queries and business intelligence
- You value operational simplicity
- You want predictable, lower costs
- Your team doesn't have dedicated platform specialists
- You want to move fast without drowning in configuration
The Call to Action
If you're a business leader, here's what I'm asking:
Before your organisation commits to (or renews) an enterprise data platform, require your IT team to evaluate ClickHouse Cloud + Power BI as a baseline comparison.
Not as a foregone conclusion — as a genuine evaluation.
Ask them to:
- Run a proof of concept with a representative workload
- Document the total cost of ownership for both options over 3 years
- Compare time-to-value for delivering initial capabilities
- Assess operational requirements — how many people are needed to maintain each option
- Evaluate performance on your actual query patterns
If the enterprise platform wins that comparison fairly, proceed with confidence. But don't assume complexity equals capability. Don't let "nobody got fired for buying [enterprise vendor]" drive a decision that affects your organisation's agility and costs for years.
The data industry has been selling enterprise complexity to organisations that would be better served by simplicity.
It's time to question that assumption.
Getting Started
If you want to evaluate this stack:
- ClickHouse Cloud: clickhouse.com/cloud — free tier available
- Airbyte Cloud: airbyte.com — free tier available
- Power BI: powerbi.microsoft.com — Pro trial available
- LibreChat: librechat.ai — open source
- ClickHouse MCP: github.com/ClickHouse/mcp-clickhouse
You can have a working proof of concept in days, not months.
The question is whether you'll take the time to try.
Have you evaluated ClickHouse for your analytical workloads? I'd be interested to hear about your experience in the comments — both successes and challenges.
Top comments (0)