Use cases
Why teams choose Yres. The four pain points we encounter most often with data teams — and a real-world example that shows what changes when you solve them with Yres.
1. Unreliable dashboards
Reports lag behind or are inaccurate, and a lot of time goes into checking and correcting them.
With Yres: data is loaded automatically and stays consistent. The load engine keeps full SCD2 history per table (via KeyHash/RowHash and IsCurrent), so changes are written cleanly as a new version instead of overwriting existing records.
2. Fragile pipelines
Scripts break and no one knows exactly what happens when something changes.
With Yres: fixed structures and transparent dependencies. You describe what you want to load as metadata; Yres generates the corresponding ADF pipelines and linked services. Object-level lineage (tables, views, procedures, functions, materialized views) makes it clear what a change affects.
3. Too much manual work
Every new data source costs time and custom work.
With Yres: connect new sources via wizards, without rebuilding. At its core, adding a source means writing metadata rows — Yres translates those into the ADF pipelines that do the work.
4. Dependent on a single person
Knowledge sits with one engineer or consultant.
With Yres: a transparent and transferable data platform. The configuration lives in the web app and in the database, not in the heads of individual engineers.
Real-world example: before and after Yres
The example below is illustrative and hypothetical — a mid-sized retail company, not an existing customer. It comes from the Yres training material and gives a recognizable picture of what changes.
Before Yres
A mid-sized retail company had its data scattered across systems:
- sales in one database;
- marketing campaigns in another;
- inventory in spreadsheets.
The consequences:
- IT spent days every month manually merging and cleaning the data.
- Managers had to wait for reports and worked with outdated information.
- The company paid for large VMs to run the processing — even during quiet periods.
After Yres
Within a few days, all sources were connected:
- sales, marketing, and inventory were updated automatically and ready for dashboards;
- managers gained near real-time insight and could adjust campaigns immediately or replenish inventory right away;
- thanks to database scaling, the company paid for extra capacity only during heavy processing;
- Yres's architecture handles large volumes without large, expensive VMs.
The result
| Aspect | Before Yres | After Yres |
|---|---|---|
| Reporting time | days | minutes |
| Decision-making | waiting on reports | faster, on current data |
| Monthly Azure costs | high (constant large VMs) | lower (scale on usage) |
| Additional technical staff | needed | not needed |
So reporting time dropped from days to minutes, decision-making became faster, and the monthly Azure bill went down — "all without adding technical staff."
The cost savings come from automatic database scaling (settings AutomaticDatabaseScaling, DefaultServiceTier, HighServiceTier): the database scales up during a heavy load run and back down afterward. Throughout, Yres runs entirely within your own Azure environment.
Recognizable from practice
The following scenarios are illustrative and show how Yres helps in a range of situations:
- A manufacturing company migrated from an on-premise data warehouse to Azure and had to redesign its entire connection structure. Previously every application was connected differently; with Yres this is standardized.
- A housing association struggled with reports that didn't work well and a long "time to repair." With Yres: faster troubleshooting, shorter resolution time, and always up-to-date data.