Data Warehouse Development
Data warehouse development
One place where the numbers agree: modelled tables, incremental loads, and history that does not silently change.
The problem
Every Monday someone downloads the previous week's sales from the marketplace, pastes it into the master sheet, and overwrites last week's version. Six months from now, if someone asks what revenue was in March, the answer depends on when they pull the file and whether the column names have changed since then. A data warehouse is simply a database designed around this problem: it loads only what is new or changed, keeps the old rows intact, and models the tables so that the same question always returns the same answer regardless of when it is asked. For a small business, this is usually overkill — until suddenly it is not, and the moment you notice is usually an audit or a funding conversation.
Last month's number looks the same whether you check today or in six months.
Built for: Businesses whose reporting is slow, inconsistent, or built on exports that overwrite themselves.
What we deliver
Data modelling first. We design the tables before writing a line of code — what the facts are, what the dimensions are, and how they relate. A poorly modelled warehouse is nearly as frustrating as no warehouse, and changing the model later is expensive.
Incremental loads. Only new or changed rows are processed on each run. Full reloads that lock the database for an hour are avoided by design, not as an afterthought.
History that does not change. When a customer's address or a product's price changes in the source system, the old value is preserved alongside the new one. Reports for last quarter use the values that were correct last quarter.
Agreed definitions baked in. The transformations from raw source data to reportable numbers are written in SQL that anyone can read and audit. There is no magic in a spreadsheet formula that one person wrote and nobody else understands.
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Not sure which of these fits? See the whole data engineering practice, or read what we build for your industry.
Tell us what’s slow.
Describe the job eating your team’s day. We’ll tell you straight whether an agent is the right fix — and if it isn’t, we’ll say so.
Frequently asked questions
- Do we need a data warehouse if we are a small team?
- Probably not yet. If your reporting still fits in a well-structured sheet updated by one person, keep the sheet. The honest trigger for a warehouse is when you have more than two or three data sources that need to be joined, or when your current reporting takes more than half a day to assemble by hand each week.
- Where does the warehouse run — on our servers or in the cloud?
- Either. For most small Indian businesses, a managed cloud database keeps costs low and removes the maintenance burden. If data residency requires on-premise, that is a constraint we design around from the start. We do not choose the infrastructure before understanding the requirement.
- What if our source systems change their exports or rename columns?
- That is one of the most common ways a pipeline breaks, so we build a validation layer that detects unexpected schema changes and alerts before bad data is loaded. It will not prevent the source system from changing, but it will make sure you know immediately when it does.