Blog
- This is why we need data engineers
Will AI replace data engineers? We built Ask AI into DeltaVault, then recorded it losing a race to a data engineer building one view by hand.
- Keep the model stable, change the template
Keep the model stable, change the template: metadata-driven code generation turns one governed model into native Databricks code, with other platforms next.
- The Mapping Is the Deliverable
The source to target mapping that turns a source system into the business model is usually never written down. Map & Match records it as reviewable metadata.
- AI drafts the tedious part. You keep the decision.
AI-assisted data modeling in DeltaVault: skills draft keys, joins, views and descriptions as proposals you read first. Nothing writes until you accept.
- Meaning Leaks at Every Handoff
Business definitions in one tool, the technical data model in another, lineage in a third: meaning leaks at every handoff. DeltaVault keeps them in one model.
- The template engine is yours
DeltaVault's data warehouse automation templates are versioned, team-owned, and open to your own AI assistant, so your standards live in templates you control.
- AI that explains how reality became data
AI governance for data platforms starts with one loop. Every DeltaVault AI action follows the same five steps: context, content, instruct, review, refine.
- You already own the stack
You already own the lakehouse, storage, and orchestration. The missing layer is data warehouse automation: business meaning in, Data Vault and workflows out.
- Edit the template, watch the code change
Jinja template code generation you can see: open DeltaVault's template playground, edit the template, and watch the generated code update live.
- Manage data, not plumbing
Declare table metadata once; DeltaVault generates create-table, merge and workflow code for Databricks, Snowflake or Fabric. That is data warehouse automation.
- The Medallion Three-Body Problem
Without an agreed business model, bronze, silver, and gold drift apart: three suns pulling the medallion architecture into a three-body problem.
- Should Software Be Free?
Free software still burns hosting, AI tokens, and human hours. A working position on honest software pricing and a free tier for the Data Vault community.
- Data Vault Needs New Rules for Lakehouse
Columnar storage, generated views, and AUTO CDC took over every load reason for Data Vault satellite splitting. What is left is business meaning.
- The Lakehouse Ended One Satellite Per Source
One satellite per source is a row-store habit. On Delta Lake width is cheap and joins are what you pay for: consolidate Data Vault satellites by meaning.
- Show Me Your Silver Layer
Any AI tool can land data in your bronze layer. The medallion architecture's real test is an integrated silver layer, and that is a people problem.
- Your Database Is Not Your Business
AI can infer a semantic layer from your technical exhaust. It cannot define your business ontology. Why a system of record is not a system of meaning.
- No Human, No Loop
Human in the loop puts a person at the end of an AI process as a checkpoint. In DeltaVault the human starts the loop, so there is no loop without one.
- The Model Says Yes. The Source Says No.
Your conceptual model says delivery address. Your source says address type. Describe the view in one sentence and keep column-level lineage on the result.
- Stop Asking Data Engineers to Paint
A conceptual data model breaks on first contact with a real source system. The fix is not better artists: it is a model that works like metadata.
- The Meeting Is the Model
Turn a Teams meeting transcript into a conceptual data model in minutes, with every proposal traceable back to what was said in the room.
- How raw is your Data Vault?
In a raw vault, raw describes the data, not the structure. Why source-shaped vaults never integrate, and how to rebuild yours without starting over.
- AI Data Modeling: Enough Talk. Let's See It in Action
The AI business modeling workshop drafts concepts, relationships, and facts from your own content, shows its reasons, and writes nothing until you apply.
- How to stop the AI Guessing
The AI business modeling workshop runs seven passes and eleven consistency checks over your stories before it proposes a single concept. Here are the rules.
- Where, Oh Where, Has Integration Gone?
An AI can model every source you own and still leave your warehouse un-integrated. Data integration is a decision about business keys, and AI can draft it.
- AI does not fix bad data models
AI data modeling aimed at your source schema returns a faster, shinier source schema. Business meaning comes from your own material, and sources map into it.
- Give AI Content, Not More Prompts
Upload, consolidate, and research business content into DeltaVault's AI knowledge base, then review every draft before it shapes your business model.
- Built, Not Painted
When the source does not hold the model's shape, AI helps build the bridge: discovered relationships, drafted views, and column-level lineage end to end.
- AI-native, not AI-added
What separates an AI-native data catalog from a bolted-on chat panel: governed writes, and AI skills you can open, edit, and extend.
- Build or buy: the cost AI hides
Build or buy a data platform? With AI-assisted coding, building your own looks cheap. The code was never the expensive part: owning it is.
- Business context is not an AI prompt
Business context for AI in DeltaVault is not what you pasted into the chat box. It is a governed setting every AI skill reads, written once in your own words.
- Meaning before machinery
AI made pipelines cheap to build. The scarce thing now is business context for AI: what your business means. DeltaVault captures it once and builds from it.