The new load on your warehouse

If your platform is now facing outward, you’ve probably noticed a few things:

• Queries are triggered at all hours across time zones.
• Dashboards are refreshed constantly, often automatically.
• APIs and applications hit your platform thousands of times a day.
• Bills rise in line with access, not necessarily in line with the value delivered.

The result is a warehouse that’s always on, supporting workloads far outside its original remit. Even efficient queries become expensive when they run repeatedly at scale.

Why caching and concurrency aren’t enough

Modern warehouses like Snowflake, BigQuery, and Databricks have been designed to handle concurrency and offer features like caching and materialised views. These are fantastic for internal BI, where users share similar dashboards and filters.

But customer-facing scenarios are different:

• Every user brings unique filters and contexts.
• Authentication breaks caching patterns.
• APIs and integrations trigger fresh queries each time.

In these environments, the warehouse isn’t struggling because it’s weak, it’s struggling because it’s doing a job it was never optimised for.

A better pattern: separate access from processing

Rather than asking your warehouse to do everything, a more sustainable model is emerging: separating processing from serving.

This means keeping your core data platform focused on what it does best… Governance, transformation, and trusted data pipelines, while introducing a dedicated query layer for high-frequency access.

Tools like ClickHouse are designed for this role. Originally built for real-time web analytics, it thrives under high-concurrency workloads and delivers near-instant responses, even when handling large-scale, read-heavy queries.

By placing a serving layer alongside your warehouse, you can:

• Support customer dashboards, APIs, and portals without burdening the warehouse.
• Keep costs predictable by avoiding compute charges for every refresh.
• Scale efficiently as usage grows, without sacrificing governance or data trust.



For greenfield use cases like analytics products or real-time reporting platforms, ClickHouse can even stand alone. But for most established enterprises, it’s a complement, not a replacement.

Why this matters now

Data usage is no longer confined to analysts and executives. Your customers, partners, and digital products all depend on timely, reliable insights.

This means your platform has to handle:

• Continuous demand rather than peak-and-trough access.
• External users who expect sub-second responses.
• Costs that rise with every additional query, regardless of whether the data changed.



By adapting your architecture, you’re not just improving performance, you’re aligning your platform with how data is actually being consumed today.

Questions to ask

If your warehouse is now serving both internal and external audiences,

take a step back and ask:

• Are you confident your cost model will scale with demand?
• Do external workloads compromise performance for internal teams?
• Are you treating your warehouse as a one-size-fits-all solution?



If any of these hit close to home, it may be time to rethink the balance between processing and serving.

The takeaway

Data usage has shifted from predictable, internal-only access to continuous, external demand. Warehouses still matter, but they no longer need to carry every workload on their own.

By introducing a serving layer alongside your warehouse, you create an architecture that reflects modern realities, supporting trusted data internally while delivering efficient, scalable insights externally.



It’s a small change with a big impact: better performance, lower costs, and a platform designed for how your data is actually used.