# Redshift compute cost optimization

Suhas Narayan | Business Intelligence Engineer

Sanitized portfolio case study prepared for portfolio review.

## Business challenge

A Redshift cluster served dashboards querying S3 through Spectrum. Assessing the right configuration required understanding active query demand, concurrency peaks, and memory needs separately from the historical data footprint.

## My contribution

I investigated storage architecture, utilization, and BI concurrency; compared configuration costs and memory capacity; and recommended a smaller two-node configuration with headroom for peak dashboard workloads.

## Decisions & methods

- Sized against the workload being queried. Distinguished approximately 25.7 billion historical snapshot rows from 106 million latest-snapshot rows, establishing the relevant consumption footprint for the sizing analysis.
- Investigated peak demand rather than relying on averages. Reviewed dashboard query bursts and connection patterns, distinguishing four-to-five simultaneous queries from up to seven open connections.
- Balanced savings against memory pressure. Compared smaller configurations and recommended 32 GiB of memory per node over a 16 GiB option to retain more headroom for concurrent joins.
- Made the financial trade-off explicit. Compared the existing and recommended configurations, quantifying the opportunity to reduce monthly compute costs from approximately $4,760 to $1,585.

## Outcome

The resulting configuration reduced confirmed monthly Redshift compute costs by approximately $3,175, equivalent to $38,100 annualized and a 66.7% reduction, rounded to 67%.

## Results & context

The recommendation reflected a Spectrum-based architecture with data stored in S3 and compute demand driven by BI query activity. Configuration selection considered both cost and memory capacity for peak workloads.

## Methods & Tools

Redshift Spectrum, Workload profiling, Concurrency analysis, Cloud cost modeling, Configuration trade-offs, BI infrastructure