A data warehouse built around your questions
not a generic schema nobody can query
Most businesses have their data spread across an ad platform, a CRM, a store and a spreadsheet, each with its own definition of a conversion. We build the ETL pipeline and warehouse that pulls those into one place on a schedule, with a shared definition of the numbers that matter.
What it is
A data warehouse is a single place where data from several systems lands in a shared, queryable structure, and an ETL (or ELT) pipeline is the scheduled process that gets it there: extracting from each source, transforming it into a consistent shape, and loading it into the warehouse. The point is not storage, it is agreement: once ad spend, CRM deals and store orders live in one schema with one definition of a “sale,” a report built on top of it means the same thing to everyone who reads it.
When you need it (and when you do not)
You need this once you are pulling the same numbers from three different places and getting three different answers, ad platform reports one conversion count, the CRM shows a different deal count, and the store’s own dashboard disagrees with both, because each system defines the event slightly differently. It is also the right build once someone is manually exporting and joining spreadsheets every week to produce a report that should take five minutes, that time cost alone usually justifies the project within a quarter.
You do not need a full warehouse if you have one data source and a BI tool that can connect to it directly, that is simpler and cheaper. A warehouse earns its cost once there is real joining to do across systems that were never designed to talk to each other.
How we build it
We model the schema around the actual questions you need answered, revenue by channel, cost per lead by source, retention by cohort, rather than a generic star schema nobody ends up querying the way it was designed. Extraction runs on a schedule matched to how fast each source actually changes: hourly for ad spend, daily for CRM and order data, and we use incremental loads so a daily sync processes what changed, not the entire history every time. PostgreSQL covers most mid-size setups well; for larger volumes or when query speed on years of historical data matters, we use a columnar store like ClickHouse instead. Every transformation step is written in version-controlled code, not inside a BI tool’s proprietary modeling layer, so the logic is portable if you ever change reporting tools. We built this exact pattern for a two-brand operation that needed one analytics view across otherwise separate systems.
What to watch
The most common failure in a warehouse project is skipping the step where you validate new numbers against what you already trust, a warehouse that quietly disagrees with your existing reports for a month before anyone notices is worse than no warehouse at all. We run a validation period against known numbers before anyone treats a new dashboard as the source of truth. The ongoing cost of ownership is mostly about source stability: a CRM or ad platform API change can break an extraction step, which is why data quality checks and alerts are part of the initial build, not an afterthought. Vendor lock-in is low when the warehouse is PostgreSQL or ClickHouse and the transformation logic lives in your own code; it rises sharply if you let a BI tool’s proprietary semantic layer become the only place the business logic exists.
Naming conventions matter more than they sound like they should: a warehouse where “revenue” means something slightly different in two tables will eventually produce a report that is confidently wrong, which is why we document every metric’s exact definition alongside the schema itself, not as a separate wiki page nobody opens once the project ships. We also agree with you on who is allowed to add a new metric definition later, so the warehouse does not quietly accumulate five slightly different versions of “active customer” within a year of handover.
Price and timeline
| Scope | Price | Timeline |
|---|---|---|
| 2-3 sources, core reporting | from $2,500 | 3 to 4 weeks |
| 5+ sources, historical backfill | from $6,000 | 5 to 6 weeks |
Related
Built as part of custom development and analytics. Feeds directly into BI dashboards and often sits alongside product analytics setup. See it in practice in an AI analyst across two brands. Tell us which systems hold the numbers you need joined: get in touch.
FAQ
How much does a data warehouse cost?
A warehouse pulling from two or three sources, a CRM, an ad platform and an order system, for instance, starts at $2,500. A fuller setup across five or more sources with historical backfill runs $5,000 to $10,000.
How long does it take?
3 to 6 weeks for most setups: understanding what each source actually means by its fields, building the extraction and transformation logic, and validating the numbers against what you already know to be true before anyone relies on a new dashboard.
What is the stack?
PostgreSQL as the warehouse for most mid-size setups, with Python-based extraction scripts or a tool like Airbyte for standard sources. For larger volumes we use a columnar warehouse like ClickHouse when query speed on historical data matters.
Who owns the warehouse and the data?
You do. It runs on your infrastructure or your cloud account, and the transformation logic is version-controlled and documented, not a black box inside a BI tool's own engine.
What if a source changes its API or export format?
We build data quality checks that flag a broken or unexpectedly empty extraction before it reaches a report, the same schema-change alerting we use in our integration work generally, so a silent gap gets caught in hours, not discovered a quarter later.