Data Warehouse Management
A warehouse you own, and can afford.
Redshift, BigQuery, Snowflake or Postgres — chosen for your volume, stood up in your own cloud account, and then actually managed: modeled schemas, scheduled transformations, partitioning and sort keys that match how you query, and a cost line that gets watched. The warehouse is the layer every other service reads from, so it is treated as infrastructure rather than as a place exports go to rest.
Data Warehouse Management stands up Redshift, BigQuery, Snowflake or Postgres in your own cloud account, chosen on your volume, not our preference. Schemas are modeled, transformations are documented and tested, partitioning and sort keys follow how you query, loads alert on failure, and cost is monitored per workload and reviewed monthly.
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The tension
Either there is no warehouse and every question becomes an export, or there is one and nobody owns it — tables nobody can explain, a query that takes four minutes, and a cloud bill that grew every quarter without anyone deciding it should.
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The shift
One modeled warehouse in your account, with a documented schema, transformations that run on a schedule and are tested before they land, and physical design — partitions, sort and distribution keys, clustering — chosen against the queries you actually run. Cost is monitored per workload, so an expensive query is a thing somebody notices.
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The outcome
Questions get answered in seconds by people who are not engineers, the schema is documented well enough for a new analyst to be useful in a week, and the bill is a number you set rather than one that arrives.
What you get
Concrete, not fluff.
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Platform chosen on your volume, not our preference
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Modeled schema with documented, tested transformations
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Partitioning, sort and distribution tuned to real queries
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Scheduled loads with failure alerting and backfills
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Role-based access, retention windows and audit trails
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Cost monitoring per workload, reviewed monthly
How it works
Brief in, live campaign out.
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Size the workload and pick the platform
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Model the schema
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Load, transform and test
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Tune for the queries you run
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Watch cost and health continuously
It loops — every cycle feeds the next.
The proof
Infrastructure is judged when it is boring.
Loads landing on schedule, failures caught and backfilled, and query cost holding flat while volume grows.
A retailer on Redshift was running everything from one wide table and paying for the scan every time. Remodeling into partitioned facts with sort keys matched to the common date-and-channel filter cut typical query time from minutes to seconds and took roughly 40% off the monthly warehouse spend. Directional, not a named client.
FAQ
Frequently asked questions
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Whichever you already run, in most cases. If you are starting fresh we recommend on volume, concurrency and what your team already knows — Postgres is often the honest answer for a business that has been told it needs Snowflake. We will say so rather than sell you the bigger thing.
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Yours, under your billing, with us granted access. You keep the data, the credentials and the ability to end the arrangement without an export project. We have never thought a vendor holding your warehouse hostage was a business model worth having.
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Usually, and it is a common starting point. The first pass is an audit: what loads, what breaks, what costs money and what nobody has queried in six months. You get that as a written picture before anything gets changed.
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Cost is monitored per workload and reviewed monthly, and the physical design work is largely cost work — a query that scans less costs less. Where a job is genuinely expensive, you get told what it buys so it can be a decision.
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Not to run it. If you have them, they typically keep ownership and we build to their conventions. If you do not, it is documented and handed over so that hiring one later is not a rescue mission.