For years, most analytics ran on yesterday's data: a nightly batch job copied tables into a warehouse, and reports caught up the next morning. That is changing. Customers expect live order tracking, fraud checks happen during the transaction, and AI features are only as good as the freshness of the data behind them.
The pieces have matured
Real-time used to mean building and babysitting a lot of bespoke infrastructure. Today the core patterns are well understood and widely supported:
- Change data capture reads changes straight from an operational database's log and publishes them as a stream of events, without touching the application.
- Kafka-compatible streaming platforms, many of them available as managed services, carry those events reliably between systems.
- Stream processors such as Apache Flink join, aggregate and enrich events as they flow.
- Open table formats such as Apache Iceberg let the same tables serve streaming writes and batch analytics, so there is no longer one copy of the truth for each.
Contracts keep it trustworthy
Streams make it easy for a change in one system to break ten others silently. Data contracts — an agreed, versioned schema and set of expectations for each stream — turn those breakages into failed checks at the source rather than wrong numbers on a dashboard weeks later.
Real-time is a choice, not a virtue
Streaming adds operational cost and complexity. For each use case, ask how fresh the data genuinely needs to be. Payments, logistics and personalisation often need seconds. A monthly board report does not. Most organisations end up with both, sharing the same foundations.
Start with one flow
Pick a single, valuable stream — orders, sign-ups, sensor readings — and take it end to end, from capture to a live metric someone uses. The patterns you establish there are the ones every later stream will follow.
Our Data Analytics & Business Intelligence practice designs these pipelines around the decisions they serve, and our Cloud, DevOps & Infrastructure team runs them reliably.