Schema mapping
Map source columns to a growing canonical schema, with alternatives, aliases, and manual review.
Data mastering for teams with messy datasets
Turn scattered data into mastered datasets.
daitalake helps you upload datasets, review suggested mappings and cleaning rules, link related records, and download processed master datasets — without building a data engineering team first.
Upload datasetsCSV and spreadsheet-first intake.
Review decisionsApprove, edit, and guide processing.
Download mastersReceive cleaned, linked outputs.
Capabilities
From raw files to governed data products.
Designed for teams who need quality data foundations before analytics, investigation, automation, or reporting.
Cleaning rules
Standardise messy values like phone numbers, email, dates, addresses, and common placeholders.
Cross-dataset linking
Identify shared identifiers and candidate matches across multiple datasets, then review before mastering.
Master records
Build cleaner entity-level outputs that combine duplicate and overlapping records with source provenance.
Relationship context
Capture links between people, organisations, places, phones, and identifiers where supported by the data.
Output packages
Download processed master datasets and supporting summaries for review, reporting, or downstream systems.
Workflow
A guided data-quality service.
Start with files. End with cleaner, linked, mastered data and a clear record of what changed.
Service model
Initially managed for you. Flexible when you need control.
daitalake will launch as a website service: upload your datasets, review key processing choices, and download mastered outputs. For teams with stricter infrastructure needs, deployment options can extend into your own VPS or on-premises environment.
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Managed web service
Fastest route for small teams: submit datasets and receive processed outputs through the daitalake portal.
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VPS deployment
Run the service in your controlled virtual server environment when you need tighter ownership.
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On-premises option
For teams who need infrastructure kept inside their existing operating environment.
Deployment options
Choose the right operating model.
Start simple, then move closer to your infrastructure when your data programme matures.
Managed website service
Users upload datasets through daitalake, receive processing updates, then download the mastered output package.
Customer VPS
Deploy daitalake into a customer-controlled VPS for closer network, storage, and operational control.
On premises
Run daitalake inside an existing infrastructure boundary with tailored deployment and support.
How it works
Four simple steps.
Upload
Add datasets through the portal or register existing sources.
Review
Approve or adjust suggested mappings, cleaning rules, and linking strategy.
Process
daitalake cleans, links, masters, and packages the data with supporting evidence.
Download
Receive a mastered dataset package ready for downstream use.
FAQ
Early questions.
Is daitalake live?
This is an early placeholder site for the upcoming product. Use the contact section to register interest or discuss a pilot.
What kinds of datasets will it support?
The initial service is designed around file-based datasets such as CSV and spreadsheets, with processing focused on mapping, cleaning, linking, and master outputs.
Can users change suggested mappings or rules?
Yes. The intended workflow includes review points where users can approve, edit, or request revisions before processing continues.
Can daitalake run outside the website service?
Yes. The roadmap includes customer VPS and on-premises deployment options for teams that need a more controlled environment.
Early access
Discuss a pilot or join the launch list.
Tell us what kind of datasets you need to clean, link, and master. We will use early enquiries to shape the first service packages.
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Ready to turn datasets into a mastered data product?
Start with a managed service, then choose VPS or on-premises when you need more control.