Data Analytics & Engineering

Almost always because the same term is defined differently in each one — each report encodes its own version of what counts as active, or complete, or revenue. The fix is a modelled layer where those definitions live once and every report derives from it, rather than adjudicating between dashboards one at a time.

If reporting queries are slowing your production database, if you need to combine data from several systems, or if you need history that operational systems overwrite, then a separate analytical store earns its place. If none of those are true, a well-indexed read replica may be all you need — and it is a great deal less to run.

Less often than people assume. The honest test is whether anyone would act differently on data that is an hour old — if not, a batch pipeline is cheaper, simpler and more reliable. Streaming is worth the additional complexity for genuinely time-sensitive decisions like operational monitoring or fraud signals.

Yes. Most of the value is in the pipelines and the modelled layer beneath the tool, and that work carries across regardless of which tool sits on top.

By making it visible rather than silently filling gaps. Automated checks on freshness, row volume and value distribution catch a broken upstream feed as an alert, which is considerably better than discovering it through a decision made on stale numbers.