Redshift consulting that treats the warehouse as a product
Amazon Redshift rewards teams that design for it and punishes teams that treat it like a big Postgres box. It is a columnar, distributed engine: how you distribute tables across nodes, how you sort them, and how you load them determines whether dashboards return in one second or ninety. DEV3LOPCOM, LLC — an Austin, Texas consultancy, 100% onshore — builds and repairs Redshift warehouses so the analytics on top of them stay fast as the data grows.
Our founder is a former Tableau Software Professional Services consultant, and most of our Redshift work exists to serve a BI layer. That perspective matters: we tune the warehouse against the queries your dashboards actually run, not against synthetic benchmarks.
How an engagement runs
We start with an assessment: system tables and query history tell us where the time goes — disk-based query steps, skewed distribution, missing sort-key benefit, commit queuing, WLM misconfiguration. We review your table designs, load patterns, and concurrency profile, and check whether your spend matches your usage (including whether Serverless or RA3 nodes fit better than what you’re on). Then we design: a written plan covering table redesigns, workload management, pipeline changes, and migration steps if you’re moving in from another platform. We build it with validation at every step — row counts, checksums, parallel-run comparisons — and hand off with documentation your team can operate from.
Common Redshift problems we fix
- Distribution key skew: one node holds far more of a big table than the others, so every query waits on the slowest slice. We rebalance with better keys or
DISTSTYLE AUTOwhere it genuinely fits. - Sort keys that no query uses. A sort key only helps when predicates align with it; we set them from your actual query history.
- Loading with row-by-row
INSERTs instead ofCOPYfrom S3, which is slower by orders of magnitude and bloats commit overhead. - Vacuum and analyze neglected, so deleted rows linger and the planner works from stale statistics.
- BI tools issuing
SELECT *against wide tables — costly on a columnar store that would otherwise read only the needed columns. We fix the models and the Tableau workbooks together. - Fragile ingestion scripts that fail silently. We rebuild them as observable, scheduled pipelines with retries, alerts, and backfill support.
Why dev3lop
Scale is not theoretical for us. We built pipelines handling trillions of records at Nielsen and cloud data infrastructure for Buxton, where GCP pipelines fed more than 100 analyst workbooks. The pattern transfers directly to Redshift: model the data for how it will be read, automate the loads, monitor freshness, and document everything so the warehouse outlives any single engineer.
We also work both directions — migrating teams into Redshift from legacy on-prem warehouses, and helping teams whose Redshift bill has outgrown its value evaluate alternatives honestly.
Get a warehouse assessment
If your Redshift queries are slowing, your load window is stretching, or your AWS bill is climbing without explanation, start with an assessment. We’ll return a prioritized plan with the quick wins separated from the structural work. Contact us and include a sentence about your worst query — that’s usually the best starting thread.