Data Engineering is the easiest sales job in the world. The key driver for Data Warehouse/Lake bills will always be the amount of compute used. Put shortly:
1 | NUMBER_OF_COMPUTERS * TIME = YOUR_BILL |
So a Data Engineer’s job ends up being aligning common sense, complimentary interests:
- Use less computers, so business is happy with the bill.
- Finish jobs FAST, which thrills the data science, analyst, and business stakeholders.
- When jobs are fast and non-resource intensive, they are more resilient – meaning Data Engineers can sleep through the night!
“Cheaper, faster, and Better” – choose any three! The following are high-level principals which will build your intuition on how the resources you already pay for can be improved.
Start with idle capacity. Lower-risk savings can begin with everyday operating settings. Automatically shutting down idle compute and allowing excess capacity to scale down can reduce waste.
Measure the cost of finishing the work. “Right-sizing” means comparing the total cost of completing a workload. A smaller cluster that runs much longer may cost more; a larger cluster earns its keep only if its speed offsets its higher rate. Choosing compute suited to the workload—and testing available acceleration options—can reveal savings without rewriting business logic.
Get more useful work from each cluster. A cluster is a group of computers providing processing capacity. Giving every workload its own cluster can leave much of that capacity unused. Where there is spare capacity, compatible queries and workloads may run together without missing their deadlines. The goal is better utilization while preserving room for demand spikes.
Coordinate workloads and schedules. Two workloads starting at the same time do not necessarily need separate clusters. Related tasks can share resources within a coordinated workflow, reducing cost.
Measure freshness, not just frequency. Starting a job more often does not necessarily deliver usable data sooner. If the source updates every fifteen minutes, checking every minute may add expense without adding information. If processing takes longer than the interval between runs, more frequent launches can create overlap or queues. Scheduling should follow the complete journey from source update to usable result—and the business requirement for when that result must arrive.
Remove unnecessary work. Repeatedly rebuilding an entire dataset, calculating the same result for several teams, or refreshing unused reports all deserve scrutiny. Processing only what changed, reading only the data required, and reusing common results can reduce computing demand. These changes require checks that the results remain correct.
A focused consulting engagement can turn these opportunities into measurable improvements. Start with actual spending, utilization, data freshness, and failure rates. Identify suitable changes, test them against existing requirements, and roll them out in stages.
For leadership, the purchasing decision becomes concrete: a defined scope, measurable acceptance criteria, and savings assessed alongside implementation and ongoing operating costs. Verify that efficiency gains reduce billed usage or avoid future capacity purchases. Success means the business receives the data it needs, when it needs it, with less waste and dependable operation.
Further reading