The problemExcel stops at 1,048,576 rows. Your data didn't, so your totals are wrong.
In shortData warehouse consulting means setting up one analysis database, such as Postgres, BigQuery, Snowflake or ClickHouse, and the pipelines that copy in data from your CRM, store, ads and finance tools, so reports stop relying on CSV exports. We build right-sized warehouses for small and mid-sized businesses from $4,000, typically in 3–8 weeks for a few sources.
Data warehouse consulting usually starts with a spreadsheet that gave up. Excel crashes or stops at 1,048,576 rows, someone spends Monday exporting CSVs from five tools, the app slows down whenever a report runs, and Shopify and QuickBooks disagree about last month. A data warehouse is a database built for analysis rather than for running your app, and it fixes all four.
We set up warehouses and the pipelines that feed them, sized honestly. Most small and mid-sized 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. Everything is built in your own cloud account, with documentation another engineer can follow.
Maintained by Haider Ali, Founder & CEO · Updated
The signs people search for just before they look for a data warehouse, and what each one costs.
Unsaved work is lost, files will not open, and rows past the limit never load, so the analysis quietly leaves data out.
What we doThe data lives in a warehouse that handles millions of rows in seconds. Excel, Sheets or a dashboard connects to small summary tables instead of raw exports.
Hours of an expensive person's week go into copy-paste. Reports arrive late and stale, and one bad paste breaks the whole workbook.
What we doScheduled pipelines copy every source into one warehouse. The report refreshes itself, and nobody exports anything.
Customers feel the slowdown, and in bad cases a report locks tables or causes an outage while someone is just pulling numbers.
What we doA read replica or change data capture copies the data into a separate warehouse, so analysis never touches the production database.
Meetings go on arguing about whose number is right, inventory and tax decisions get made on a guess, and trust in every report erodes.
What we doEach metric gets one modeled definition in the warehouse, with tests that check totals against the source systems on every run.
Usage billing is hard to read. Publicly reported cases show a single careless query or retrying job running up $10,000 or more in a few hours.
What we doPartitioning, clustering, per-query and per-day cost limits and budget alerts set up at build time. On an existing warehouse, we find what drives the bill first.
Enterprise running costs, paid connectors and BI licenses every month for years, before the data ever needs that scale.
What we doWe right-size first: tuned Postgres, pay-as-you-go BigQuery or DuckDB for millions of rows, with dbt models that make a later move to a bigger warehouse easier.
We would rather tell you now than take the project and disappoint you.
| Starts from | $4,000 for a warehouse with a few sources and tested models |
|---|---|
| Typical timeline | 3–8 weeks for a few sources; more sources take longer |
| Best for | Small and mid-sized businesses with several data sources and no data engineer |
| Not for | One data source and a few thousand rows |
| Warehouses | Postgres, BigQuery, Snowflake, ClickHouse or DuckDB, chosen for your size and budget |
| What you own | The warehouse, pipelines, models and documentation, in your own cloud account and repository |
| Running costs | Estimated for your data before the build price is fixed |
| How pricing works | A fixed build price. More sources and reporting are typically quoted at $6,000–$20,000 |
Worth knowing: "Big data" is mostly a marketing term at small-business scale. Tens of millions of rows can be queried in seconds on an ordinary laptop with modern tools such as DuckDB. Buying infrastructure for data you might have in five years means paying for it for five years first.
BigQuery, Snowflake, Postgres or ClickHouse chosen for your size and budget, with sensible access controls.
Data pulled from apps, databases and APIs on a schedule, with managed connectors where they are worth the price and open-source or custom jobs where they are not.
A read replica or change data capture for your own databases, so reports never slow your app down.
Raw data turned into clean tables with dbt, tested for duplicates, gaps and broken relationships on every run, and checked against source totals.
Partitioning, clustering, query limits and budget alerts, so one careless query cannot run up a surprise bill.
Alerts when a source stops sending data or a refresh fails, before someone notices a stale dashboard.
What each table means, where it comes from and how it is calculated, readable by people who did not build it.
A 22-second look at the work: data from Shopify, QuickBooks and the bank loaded into one warehouse, kept away from your live app, with tested models. From $4,000, 3–8 weeks.
Excel stops at 1,048,576 rows. Your data didn’t. Rows past the limit vanish. Reports slow down the live app. Shopify, QuickBooks and bank disagree. There’s a better way. A data warehouse: one set of numbers, no CSVs. Pipelines from every tool. Kept away from your app. Tested models and cost controls. From $4,000 · 3–8 weeks · fixed quote. Get a fixed quote. pixelandoak.com/services/data-warehouse-consulting
You probably need one if two or more of these are true:
If none of these apply, you do not need a warehouse yet. A better spreadsheet or your app's built-in reports will do, and we will say so.
| 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 |
We load raw data first and transform it inside the warehouse (ELT), so when a definition changes or a bug is found, history can be rebuilt instead of lost.
| Option | Cost | Time to first report | Risk | Who owns it |
|---|---|---|---|---|
| Do it yourself with managed tools | Tool subscriptions only | Depends on your spare time | Untested models and quietly wrong numbers | You |
| Hire a data engineer | A full-time salary | Weeks to hire, then weeks to build | Hard to justify for a few sources; knowledge sits with one person | You |
| Large consulting firm | Large engagement fees | Often several months | A stack sized for an enterprise, and dependence on the firm | Varies; check the contract |
| Pixel & Oak | Fixed build from $4,000 | 3–8 weeks for a few sources | Not a full-time team: Haider directs AI agents and signs off every model | You: your cloud account and repository |
Managed services where they save you money, open-source tools where they do not.
BigQuery, Snowflake, Postgres, ClickHouse, DuckDB
Airbyte, Fivetran, dlt, custom Python and Node jobs
dbt and SQL, with tests on every run
Scheduled jobs, GitHub Actions, Cloudflare cron triggers
Looker Studio, Power BI, Metabase, custom dashboards
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 →Every system that holds data you need, how to get it out, and how fresh it must be.
Warehouse choice, data model and a running-cost estimate, with a fixed build price.
Pipelines and models built with tests, checked against the numbers your source systems report. Haider reviews it and signs off.
Documentation, monitoring and a walkthrough, all in your own cloud account.
AI agents from our Engineering, Quality & Security and Growth & Marketing divisions do the work, in parallel. Haider Ali, founder and CEO, agrees the plan with you and signs off everything before it reaches you.
Real work that uses the same skills. Every card links to something you can open and inspect yourself.
Our setups start at $4,000 for a warehouse with a few sources and tested models, and are typically quoted at $6,000–$20,000 with more sources and reporting. The build price depends on the number of sources, whether they need custom connectors, and how much modeling is involved. Running costs at small-business scale can be small, sometimes inside a free tier; we estimate yours before fixing the price.
Usually 3–8 weeks for a few sources. The first week goes on the source inventory and design, the middle weeks on pipelines and tested models, and the last on checking totals against your source systems and handing over. A project with fifteen sources or messy legacy data will take longer, and the quote says so.
They choose the right warehouse for your size, build the pipelines that copy data in from your apps, model the raw data into clean tables with agreed definitions, add tests, cost controls and monitoring, and document it so your team can use it. The goal is one trusted copy of your data that reports and dashboards read from.
Often, yes. Snowflake and Databricks are excellent at large scale and with many people querying at once. For tens of millions of rows, Postgres, BigQuery or ClickHouse usually do the same job for less. Moving later is possible, and building the models in dbt makes it much easier.
Find what drives the bill first, usually a few queries or jobs. Then partition and cluster large tables, stop full-table scans, shorten warehouse auto-suspend times on Snowflake, cache or pre-compute dashboard queries, and set per-query and daily limits with budget alerts. We can do this on an existing warehouse before anything is rebuilt.
Put the full data in a database, such as Postgres, DuckDB or BigQuery, and connect Excel, Google Sheets or a dashboard to small summary tables built from it. You keep working in the tool you know, without the row limit, the crashes or the split files.
Managed connectors such as Airbyte or Fivetran where they support your tools and are worth the price, 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.
You hear about it before the dashboard is wrong. Freshness checks alert a person when a source stops sending data or a refresh fails, and tests stop bad data reaching the clean tables. Most reporting refreshes hourly or daily; near real time is possible at a higher running cost.
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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