Data, AI and platformBI, AI and ML for the business
Tables that update themselves
Every hour a file of orders arrives from the website, the app and the marketplace. Muvia loads it, cleans it, checks it and updates the business-ready tables in cascade: when someone opens the dashboard, the latest file is already in.
Illustrative scenario: it describes a typical case, not a customer project.
The 5 pm dashboard should not wait for someone to press a button.
Orders arrive in waves: a file from the website every hour, one from the app, the marketplace's whenever the partner sends it. Someone downloads them, merges them, removes duplicates and kicks off the refresh. If they are in a meeting, the dashboard stays stuck in the morning.
A chain of tables that updates itself, each with its own checks and the time of its last update written on it, takes the person out of the loop without taking away trust in the data.
- Who it's for
- Data engineers and data teams; e-commerce, sales and logistics who read the dashboards; whoever manages the partners that send the files.
In Muvia, step by step
Real product screens, recorded on a project with sample data.
The video · Every hour a file of orders arrives from the website, the app and the marketplace. Muvia loads it, cleans it, checks it and updates the business-ready tables in cascade: when someone opens the dashboard, the latest file is already in.
What you get
- Nobody presses a button
- The file arrives, the chain updates, and the dashboard shows the last hour with no manual step.
- You see how fresh it is
- Each Live Query says when it read the last file: whoever reads the number knows what time it refers to.
- Bad rows do not get through
- Checks stop impossible orders in quarantine, with the reason, and the partner knows what to fix.
- Only what is needed is recomputed
- Live Queries redo only the time buckets touched by new data, not the whole history for every file.
For the technical team
How it is built in Muvia
- 1
Connect the file folder
The SFTP source reads the folder every hour and picks up only new files, which are added to the rows already loaded. This is the Bronze layer: the data as it arrived.
- 2
Clean in Silver
A query removes duplicates, types dates and amounts, maps channels to a single name and adds the category from the product catalogue.
- 3
Check and promote to Gold
A Live Query on top of Silver keeps the validated orders, with Data Checks that quarantine impossible amounts and orders with no region. A second Live Query, in cascade, sums sales by hour and channel.
- 4
Let it update
Each Live Query recomputes only the time buckets touched by new data and records when it read the last file and how many rows passed the checks. Replay recomputes the past when a rule changes.
- 5
Connect the dashboard
The dashboard reads the Gold tables: when the 5 pm file lands it is already up to date, and each query's Usage tab shows what sits upstream and downstream.
- The data it needs
- Website and app orders in hourly files, from an SFTP folder
- Marketplace orders in the files the partner drops in the same folder
- Product catalogue and categories from the PostgreSQL database
- The corrections the partner resends for rejected orders
More cases in this line
All use cases- Data science on company dataYour data scientist trains wherever they like, in their own Jupyter with their own tools. Muvia gives them governed data to start from, runs the model on a schedule next to the data, and puts the scores into dashboards, alarms and reports.
- Athena: from a question to a query and a notebookOne question in plain language and the work is done: Athena saves the query in the Lab, writes the notebook with the ranking and the conclusion, and explains from the data what happened. Everything stays in the project, can be checked, and is one keystroke away.
- Data from business systems, checked before useFiles, databases and online services connect from one catalogue, and each becomes a table with no schema to design. Before the data reaches a report, written rules keep the bad rows out and set them aside in quarantine with the reason.
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We start from a real question your business has and walk the path from source to dashboard with your systems, not a demo dataset.