We analyze data pipelines and cloud infrastructure to identify where compute, storage, and processing are being wasted — then redesign the systems generating unnecessary spend.
Most organizations know their cloud bill is increasing. Few understand which specific system behaviors are responsible. Cost monitoring surfaces the numbers — it doesn't explain the architecture decisions generating them.
Spend increases month over month with no clear connection to individual pipelines, jobs, or teams.
Pipelines reprocess entire datasets when only a fraction of records have changed. Compute runs regardless.
Millions of small files. No partitioning. Wrong storage classes for access frequency. Athena scanning full datasets for filtered queries.
Clusters sized for worst-case load, idle most of the time. On-demand pricing where spot or reserved is appropriate.
Structured analysis of your cloud spend across compute, storage, and data services. We identify the top cost drivers before any changes are made.
Review ingestion, transformation, and loading workflows. Eliminate redundant computation, implement incremental patterns, right-size compute.
Redesign data layout for query efficiency. Partition structures, file format migration, compaction strategies, and lifecycle policy enforcement.
Analyze cluster configurations and worker allocations. Identify over-provisioned resources. Shift workloads to appropriate pricing models.
Reduce the volume of data scanned per query through structural changes. Applies across managed query engines and data warehouses.
Identify duplicated jobs, redundant data movement, and repeated transformations across multi-stage pipelines. Reduce end-to-end compute cost.
We don't start with tools or dashboards. We start by understanding what the system is actually doing — where work is repeated, where data moves unnecessarily, where compute runs without purpose.
Identify over-provisioned resources, idle clusters, and jobs consuming more capacity than their workload requires.
Find full reprocessing patterns where incremental is viable. Identify repeated transformations across workflows producing the same output.
Audit storage classes, file layout, retention policies, and data formats relative to how data is actually queried and accessed.
Review architectural decisions — partitioning, data movement, orchestration logic — that compound cost across the full pipeline lifecycle.
Apply structural changes with before-and-after cost measurement. Validate that performance is maintained as spend decreases.
We'll review your infrastructure and identify where the spend is coming from before recommending any changes. No commitment required for the initial analysis.