Data Warehouse Consulting, Sized for the Data You Actually Have
A data warehouse is a database built for analysis rather than for running your app. Data from your CRM, store, product, ads and finance tools is copied into it on a schedule, cleaned, and modeled so that questions spanning all of them can be answered in one query.
We set up warehouses and the pipelines that feed them, sized honestly. Most businesses have millions of rows, not billions, and that changes what they need: often a well-tuned Postgres or a pay-as-you-go BigQuery, not an enterprise platform with an enterprise bill.
This is for you if
- ✓Reporting means exporting CSVs from five tools and stitching them together
- ✓Your app database slows down every time someone runs a report against it
- ✓You have millions of rows and spreadsheets or BI tools are struggling
- ✓You want analytics, dashboards or AI features built on one clean copy of your data
This is probably not for you if
- ✕You have one data source and a few thousand rows — a good spreadsheet or your app's own reports will do
We would rather tell you now than take the project and disappoint you.
Worth knowing: "Big data" is mostly a marketing term at small-business scale. Ten million rows fit comfortably on a laptop and query in seconds with modern tools. Buying infrastructure for data you might have in five years means paying for it for five years first.
What you get
Warehouse setup
BigQuery, Snowflake, Postgres or ClickHouse chosen for your size and budget, with sensible access controls.
Pipelines from your tools
Data pulled from apps, databases and APIs on a schedule, with managed connectors where they exist and custom code where they do not.
Modeled, tested data
Raw data turned into clean tables with dbt, tested for duplicates, gaps and broken relationships on every run.
Cost controls
Partitioning, clustering and query limits, so one careless query cannot run up a surprise bill.
Freshness monitoring
Alerts when a source stops sending data or a refresh fails, before someone notices a stale dashboard.
Documentation
What each table means, where it comes from and how it is calculated, readable by people who did not build it.
Choosing a warehouse
| Option | Good for | How it is priced |
|---|---|---|
| Postgres | Up to tens of millions of rows, and teams already using it | A fixed monthly server cost |
| BigQuery | Spiky workloads, and Google Analytics or Ads data | Per amount of data scanned, with a free monthly allowance |
| Snowflake | Larger teams, many people querying at once, data sharing | Compute time plus storage |
| ClickHouse | Event data and very fast dashboards at high volume | Your own server, or a managed cloud plan |
| DuckDB | Analysis on one machine, or over files in cloud storage | Free, and runs wherever you do |
ETL, ELT, and why the order matters
Older pipelines transformed data before loading it (ETL). Modern ones load the raw data first and transform it inside the warehouse (ELT). Keeping the raw copy means that when a definition changes or a bug is found, history can be rebuilt instead of lost. We build ELT by default.
Tools we use
Managed services where they save you money, open-source tools where they do not.
Warehouses
BigQuery, Snowflake, Postgres, ClickHouse, DuckDB
Pipelines
Airbyte, Fivetran, dlt, custom Python and Node jobs
Modeling
dbt and SQL, with tests on every run
Scheduling
Scheduled jobs, GitHub Actions, Cloudflare cron triggers
On top
Looker Studio, Power BI, Metabase, custom dashboards
Security
Least-privilege roles, column masking for personal data
Not sure whether a data warehouse is what you actually need? Send over what you are trying to fix and we will tell you straight — including if a smaller job would do it.
Ask us first →How it works
Inventory the sources
Every system that holds data you need, how to get it out, and how fresh it must be.
Design and quote
Warehouse choice, data model and running cost estimate, with a fixed build price.
Build and test
Pipelines and models built with tests, checked against the numbers your source systems report.
Hand over
Documentation, monitoring and a walkthrough, all in your own cloud account.
Questions
How much does a data warehouse cost?
Our setups start at $4,000 for a warehouse with a few sources and tested models, and usually run $6,000–$20,000 with more sources and reporting. Running costs at small-business scale are often under $100 a month, and sometimes inside a free tier.
Do we need Snowflake or Databricks?
Probably not yet. They are excellent at large scale and with many concurrent users. Below a few hundred million rows, Postgres, BigQuery or ClickHouse usually do the same job for less, and moving later is straightforward if you grow into it.
How do you get data out of our apps?
Managed connectors such as Airbyte or Fivetran where they support your tools, the vendor's API where they do not, and a read replica or change data capture for your own databases, so analysis never slows your app down.
How fresh will the data be?
Hourly or daily for most reporting. Near real time is possible for event data and operational dashboards at a higher running cost, so we match freshness to what the decisions actually need.
Who owns the warehouse?
You do. It is set up in your cloud account, the pipelines and models live in your repository, and the documentation explains it well enough that another engineer can take over.
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