/everyone trusts
We gather data from your POS, store, marketplaces, ad accounts and CRM into a single warehouse that refreshes on its own. Checks stop bad data before it reaches a report, and you are told when a source goes quiet.
One record on its way through from Tally
↓Data flows from every system through raw, clean and reporting layers. A failed test stops bad numbers before they reach a report.
Data pipelines,explained without jargon.
What is a data pipeline?
One idea at a time. Each sheet lays over the last, so the picture builds up. Tap a number to jump.
It works like a municipal water line: water from several sources is filtered at one plant and reaches every tap clean and on time.
A data pipeline is an automatic routine that fetches data from your different systems, tidies it and stores it in one place.
It runs on a schedule, such as every hour or every night.
Along the way it fixes formats, removes duplicates and links records that refer to the same customer or product.
Reports, dashboards and AI features then read from that one clean source.
Picture a business like yours,in three frames.
A typical situation, not a client story. The line in each frame goes from tangled to straight as the work is done.
A Kolkata sweets chain with outlets, a website and delivery apps
Heard yourself say this?Count how many fit.
Five things owners tell us before a project like this starts. Every one you recognise adds a tally mark.
Tap the lines you have said this year.
One fits. Worth a short call to see if Data pipelines is the right fix.
Two fit. A short call will tell you whether to do this now or later.
Three fit. This is probably worth planning soon.
Four fit. You are losing time to this already.
All five. This page was written for you.
Six parts,all handed over to you.
Your Data pipelines work is made of these six parts. The comb lights each one in turn; tap a cell to read it.
Source inventory
A list of every system, what data it holds, how it can be accessed and how often it changes, with known gaps noted.
Central warehouse
A database such as BigQuery or PostgreSQL in your cloud account, organised into raw, cleaned and reporting layers.
Scheduled connectors
Syncs built with tools like Airbyte or custom scripts that pull only new or changed data on a timetable you choose.
Transformation models
Version-controlled SQL, often written with dbt, that standardises SKUs, dates, currencies and customer IDs across sources.
Data quality tests and freshness alerts
Automatic checks for missing days, duplicate orders and impossible values, with an alert when a source has not refreshed on time.
Reporting dashboards
A small set of agreed dashboards in Looker Studio, Metabase or Power BI, built on the clean layer so every chart uses the same definitions.
- Source inventory
- Central warehouse
- Scheduled connectors
- Transformation models
- Data quality tests and freshness alerts
- Reporting dashboards
The question we hear most:Google Sheets, a cloud warehouse, or your own database?
The board turns through each option. Tap one to hold it and read the trade-off.
You have a few sources, modest row counts and a team that lives in spreadsheets.
Sheets slow down and break formulas once data grows into hundreds of thousands of rows.
You have many sources, growing history and want to plug in BI tools or AI later.
Costs are usage-based, so badly written queries can add up without monitoring.
Data must stay on infrastructure you control and volumes are moderate.
You take on database maintenance, backups and scaling.
Four legs of one relay,about 6 weeks, start to finish.
Each leg is sized by how long it usually takes. Watch the baton go round, or tap a leg to jump there.
Four things that trip a project,and the fix we fit instead.
Each fuse is a mistake we see often. Watch it blow and get replaced, or tap a fuse to read the fix.
Building dashboards before agreeing definitions
We write down what counts as a sale, a return and an active customer before any chart is made.
Overwriting raw data during cleaning
Raw data is kept untouched so you can rebuild reports when rules change.
No alert when a connector stops
Freshness checks warn you the same day a source fails to update.
Pulling full history on every run
Incremental loads fetch only what changed, which keeps API limits and cloud costs down.
Four factors,four corners of your project.
Each corner grows when that factor is on the larger side. The outline is your project's footprint, and the quote follows it.
A compact project. The smallest footprint for your quote.
Mostly compact, with one area that needs more work.
A balanced build. Half the factors on each side.
A larger build. Most areas need extra work and time.
The full footprint. Every factor on the larger side.
Data pipelines questions,answered plainly.
Search the answers. If yours is missing, ask us on the call.
Cost & timeHow long does a data pipeline project take and what drives cost?
Three to six weeks covers most small and mid-sized setups. Cost rises with the number of sources, how hard each one is to access, and how much matching is needed between them. We quote after the source inventory.
Getting startedCan you pull data from Amazon Seller Central and Flipkart?
Amazon offers the Selling Partner API for approved sellers and apps. Flipkart and many other marketplaces offer seller APIs with varying access, or scheduled report downloads. We confirm the access route for each account during the inventory.
Cost & timeWhat are the monthly running costs?
Expect charges for the warehouse, any paid connector service and the BI tool if you choose a paid one. For most small businesses these are modest, and we design queries and schedules to keep them low.
OwnershipWho owns the data and the code?
The warehouse and all cloud accounts are in your name, and the transformation code lives in your Git repository. Nothing is locked to us.
In IndiaCan the data stay in India?
Yes. BigQuery, AWS and Azure all offer Indian regions, and we set the warehouse location there when you need it. Some SaaS connectors process data abroad, which we point out.
Getting startedWill this help us use AI later?
Clean, joined data is what makes AI forecasts, assistants and summaries reliable. Once the pipeline exists, adding an AI feature is far quicker because the hard data work is done.
Getting startedHow do personal customer details fit in?
We bring in only the customer fields reports need and can hash phone numbers and emails for matching. Access to identifiable data is restricted by role. Check with your adviser about DPDP Act obligations.
After launchWhat support is there after launch?
Sources change their APIs and your business adds new channels. On a monthly plan we watch test failures, fix broken connectors and add new sources or metrics as you need them.
Works well alongside,from the same practice.
Start each morning with numbers you trust
List the systems you report from today and we will map how they could feed one dashboard.