Our organization wants to move toward real time analytics. Can we fully transition our existing enterprise architecture to structured streaming, or does batch processing still hold a distinct cost advantage?
3 answers
Transitioning entirely to structured streaming is highly feasible, but you must evaluate your business latency requirements against infrastructure costs. Streaming applications require continuously running clusters, which naturally spikes your cloud consumption bill compared to ephemeral batch jobs that terminate upon completion. However, structured streaming provides unified APIs, meaning you can write your logic once and apply it to both paradigms. For a balanced approach, many enterprises adopt a Lambda or Kappa architecture where only time-sensitive operational insights utilize streaming, while heavy historical reporting remains on a strict batch schedule.
What specific latency targets are your business teams expecting from this migration? Are we talking about sub-second operational requirements, or would a micro-batch approach arriving every few minutes suffice to satisfy your stakeholders?
Using micro-batches instead of continuous processing gives you the best of both worlds. It reduces cloud compute costs significantly while delivering data fast enough for daily business intelligence needs.
Spot on, Sandra. We adopted micro-batching with a ten-minute trigger window, and it slashed our operational overhead while completely satisfying our analytics team's demand for fresh dashboard data.
Gary, our business stakeholders are looking for data refreshes every 5 to 10 minutes for our main dashboard. We do not need true sub-second latency, so running micro-batches seems like a perfect middle ground that will keep our cloud infrastructure expenses manageable without over-engineering.