Data read again without a need
Some workflows rescanned data already processed. The repeated reads increased costs for targeted workloads without adding value to their analytical outputs.
Reduce costs on targeted BigQuery workloads
Attribute spending to BigQuery workloads and reduce scans without changing analytical results.
During a Cloud and Data transformation, the founder connected GCP billing with BigQuery workflows and reduced unnecessary scans. Optimized workloads cost 35% less, excluding any pricing-model changes.
Data-intensive organization · BigQuery FinOps and query optimization · Contributor’s role : Senior Cloud Architect and FinOps Lead
−35%
cost reduction on optimized BigQuery workloads; not the whole cloud bill
Anonymized professional work by the founder of AKITA Consulting; it is not presented as a direct AKITA Consulting contract. Outcomes apply only to the described scope and do not guarantee future results.
Case studies
The challenge
In a Cloud and Data transformation, the founder led the BigQuery FinOps workstream and monitored consumption. Several analytics jobs repeatedly processed the same data volumes. A BigQuery charge alone does not show which processing created it. GCP billing data was compared with data workflows to distinguish necessary analytical work from repeated scans.
Reduce consumption without losing correctness
Fewer processed bytes must not make SQL results incomplete or data unavailable to users. Comparable periods and workloads matter when measuring outcomes.
The solution
Review billing and pricing models
Connect GCP billing data with BigQuery workflows to attribute costs to processing. Assess potential editions and pricing separately from savings actually observed.
Inspect SQL and recurring workflows
Recurring processes were compared with data actually scanned. Redundant reads informed targeted changes to selected workflows.
Improve data layout where relevant
Partitioning and clustering were applied within the BigQuery scope. These techniques help when predicates and data distribution allow useful scan reduction; they are not universal fixes.
Make spend visible over time
GCP billing data was analyzed in BigQuery and reported daily in Power BI so teams could monitor workloads beyond a one-off assessment.
The outcomes
35% reduction on optimized workflows
Costs fell 35% on optimized BigQuery workloads. This covers those jobs, not all BigQuery spending or the overall GCP bill.
Daily visibility without counting another gain
Daily Power BI reporting tracks consumption after optimization. Reviewing pricing models is a separate decision and is not added to the observed 35%.
Lessons learned
Find useful work before changing unit prices
A pricing change does not repair SQL that repeatedly reads unnecessary data. Linking billing, query behavior and business usage helps select the right intervention.
Visibility needs to outlast optimization
Daily monitoring and named workflow owners make a return of expensive scans easier to detect.
AKITA / CONTACT
Is BigQuery spend difficult to explain?
A query and billing review can reveal which costs are addressable without extrapolating a historic outcome.
Discuss your challenge