Ask your data estate
anything.
Your catalog knows where every table, column, and pipeline lives — but only if someone goes looking. The PipelineX AI data assistant turns that knowledge into a conversation: ask in plain English and get answers grounded in your catalog, column-level lineage, and 250+ indexed migration docs. It even answers the DataStage migration questions — function translations, complexity scores, and wave order — with a link back to the exact source every time.
Why this matters
A Catalog No One Queries Is Just Another Database
Most data catalogs are passive inventories. Finding the right dataset still means knowing what it's called, who owns it, and which of five similarly-named tables is the trustworthy one. Analysts file tickets; engineers re-trace lineage by hand; auditors wait days for answers that already exist in the metadata. The assistant closes that gap — it makes the catalog answer questions instead of just storing them.
Natural language data discovery
Ask in Plain English. Get Grounded Answers.
The assistant interprets intent and reasons over relationships — not just string matches — so questions return the assets that actually answer them, with the context that produced the answer.
“Where does revenue come from?”
Returns the source tables and the exact transformations that derive the revenue column — traced through the lineage graph, not guessed from a name.
“Which reports use customer PII?”
Combines PII classification with lineage to list downstream reports and dashboards touching personal data — the question every GDPR audit starts with.
“Who owns this dataset?”
Answers with the assigned steward, the business glossary term, and the freshness of the asset — so you know who to ask and whether to trust it.
“Find a trustworthy customer table”
Ranks candidate tables by lineage depth, classification, ownership, and usage — surfacing the canonical source instead of an abandoned copy.
AI for data lineage
Lineage You Can Interrogate
Column-level lineage is the most valuable thing a data platform knows and the hardest to use — graphs with thousands of nodes don't fit in your head. The assistant sits on top of the lineage graph PipelineX builds across databases, DataStage, and cloud platforms, and lets you ask the questions you'd otherwise spend an afternoon clicking through.
- Impact analysis: “What breaks if I drop
customer.region?” lists every downstream job, table, and report. - Root cause: “Why is this dashboard number wrong?” walks the upstream path to the columns and transformations that feed it.
- Migration scoping: “What feeds this DataStage job?” returns its source connectors and upstream providers before you migrate a wave.
See the underlying graph on the enterprise data lineage page.
Now it speaks DataStage migration
A Migration Expert On Call
The assistant is grounded in 250+ indexed migration knowledge docs — every function translation, stage mapping, and connector type — so it answers the questions a migration team actually asks, with the engine's real output behind each answer.
“What's the Spark equivalent of DateFromDaysSince?”
Returns the translated expression for Spark SQL, Fabric T-SQL, or Snowflake — drawn from the 200+ function translation engine, with edge-case and lossy-conversion warnings noted.
Fabric DATEADD(DAY, days, base)
Snowflake DATEADD(DAY, days, base)
“Why is this job scored Complex?”
Explains the complexity score — stage count, custom-code density, transformation depth — and the effort tier (Simple ~4h, Moderate ~8h, Complex 24–80h) behind it.
“What order should I migrate these in?”
Recommends a dependency-safe wave order from the lineage graph, so no job moves before the upstream jobs it depends on.
“What does this Transformer become on Databricks?”
Maps any DataStage stage to its modern equivalent with limitations documented — Transformer, Lookup, Aggregator, and the rest — so you know the target shape before you build it.
See the full engine on the migration automation page.
How it stays trustworthy
Grounded in Metadata, Not Guesswork
An assistant is only useful if you can trust its answers. PipelineX grounds every response in your own catalog and lineage metadata — table and column definitions, relationships, ownership, and classifications — rather than free-associating. The underlying row data stays in your systems; the assistant reasons over the structural metadata that describes it.
Because answers are grounded, every one links back to the source asset so you can verify it. When the metadata can't support a confident answer, the assistant says so instead of inventing one. That is the difference between a demo and a tool a governance team will actually rely on.
Common questions
AI Data Assistant FAQs
What is an AI data assistant?
An AI data assistant lets you ask questions about your data estate in plain English and get answers grounded in your catalog, lineage graph, and pipeline metadata. Instead of writing queries or hunting through documentation, you ask 'where does this revenue column come from?' and the assistant answers with the actual source, transformations, and owners. The PipelineX assistant also answers DataStage migration questions from 250+ indexed migration docs and the 200+ function translation engine — for example, 'what's the Spark equivalent of DateFromDaysSince?' — with a link back to the exact source every time.
How is natural language data discovery different from keyword search?
Keyword search matches strings in table and column names. Natural language data discovery interprets intent and reasons over relationships — so 'which reports use customer PII?' returns assets connected through lineage and classification, not just objects whose names contain those words. Answers come with the source context that produced them.
Does the AI assistant help with data lineage?
Yes. You can ask lineage questions conversationally — 'what breaks if I drop this column?' or 'trace this dashboard back to its source tables' — and the assistant answers from the column-level lineage graph PipelineX builds across databases, DataStage, and cloud platforms, returning the actual upstream and downstream path.
Does the AI assistant send our data to a third-party model?
No. The assistant reasons over catalog and lineage metadata — table names, column definitions, relationships, and classifications — not the underlying row data. It is designed for enterprise governance, so answers are grounded in your own metadata and every response links back to the source asset for verification.
Who is the AI data assistant for?
Data engineers use it to trace lineage and impact without opening every job; analysts use it to find trustworthy datasets without filing a ticket; and governance teams use it to answer audit and compliance questions in plain English. It turns the catalog from a passive inventory into something the whole organization can actually ask.
Put the catalog to work
See the assistant answer questions about your estate
Book a demo and we'll point the AI data assistant at a sample of your catalog and lineage — then ask it the questions your team asks every week.