Posts tagged "ai-native-platform"
- 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.
- 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.
- 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.
- 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.
- 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.
- 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 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.
- 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.
- 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.