Latency: the metric your data team should obsess over

Ask any data team what their top infrastructure metric is and most will say uptime. Availability matters, of course. But there's another number that deserves equal attention: latency. Specifically, how long it takes from a data event occurring to that data being queryable.

Why latency gets ignored

Latency is invisible until it isn't. Dashboards load slowly, but they load. Queries time out occasionally, but they usually complete. The pain accumulates gradually — analyst frustration, delayed decisions, degraded trust in the data platform — until the team normalizes slowness as just how things are.

This normalization is costly. When analysts wait 30 seconds for a query, they run fewer queries. When dashboards take 10 seconds to refresh, stakeholders stop checking them in real time. The data platform becomes a reporting tool rather than a decision-making engine.

Two types of latency to measure

Ingestion latency measures how long between an event occurring and it appearing in your data store. Query latency measures how long a read request takes to return results. Both matter, and they compound. A pipeline with 5-minute ingestion latency and a query engine with 30-second response times means decisions are based on data that's effectively 5+ minutes old — every time.

What good looks like

For real-time applications, ingestion latency should be measured in milliseconds, not minutes. Query latency on analytical workloads — even across billions of rows — should return in under a second. These aren't aspirational targets. They're achievable with the right storage and query engine architecture.

The columnar advantage

Most traditional databases store data row by row, which is optimal for transactional workloads but slow for analytical queries. Columnar storage engines organize data by column, enabling them to scan only the relevant fields for a given query. Combined with compression and vectorized execution, this approach delivers sub-second results on datasets that would cripple a row-based system.

Setting your latency targets

Start by defining what latency means for each use case. Fraud detection might require sub-100ms end-to-end. A real-time marketing dashboard might tolerate 2-3 seconds. An overnight batch report can accept minutes. Matching your infrastructure to your actual requirements — rather than defaulting to whatever your current stack provides — is where the real optimization happens.