Enterprise Data
Catalog Platform
You can't govern, secure, or feed AI with data you haven't discovered. PipelineX automatically discovers and catalogs your entire data estate — your databases, DataStage pipelines, Databricks tables, Fabric warehouses, and Snowflake schemas — so you know what you have before you make it AI-ready. AI-powered classification, business glossary, and end-to-end lineage — all in one place, with no manual tagging required.
Business value
Why Enterprises Need a Data Catalog
Without a catalog, data assets are invisible. Engineers duplicate work, analysts distrust data, and compliance teams manually trace data flows through spreadsheets. And you can't make data AI-ready before you know it exists. A catalog is step one — the inventory that AI/ML and migration both depend on.
The Shadow Data Problem
Most enterprises have far more data assets than anyone knows. Database schemas created years ago, DataStage staging tables with no documentation, Databricks notebooks producing undocumented datasets, Snowflake schemas owned by teams that have since changed. Without automated discovery, this shadow data accumulates — undocumented, ungoverned, and duplicated. PipelineX surfaces it all in a single searchable catalog.
Compliance Documentation at Scale
GDPR, BCBS 239, HIPAA, and internal data governance policies all require documented evidence of where sensitive data lives, who is responsible for it, and how it is used. Without a catalog, this documentation is maintained manually in spreadsheets — incomplete, stale, and a compliance liability. PipelineX automates classification and ownership assignment so compliance documentation is always current.
Self-Service Analytics and AI Enablement
When analysts and AI/ML teams can search the catalog to find the right dataset, understand what it contains, see who owns it, and read lineage showing where it came from — they no longer need to raise tickets to the data engineering team before starting any analysis or selecting training data. A well-maintained catalog is the foundation of a self-service data culture, the inventory that AI and analytics workloads draw on, and a meaningful reduction in the support burden on data engineering teams.
Migration Programme Foundation
Every DataStage migration and ETL modernization programme needs a complete inventory of what exists before migration can be planned. PipelineX provides this inventory automatically — cataloging the entire DataStage estate so migration wave planning has a reliable, structured foundation rather than relying on undocumented tribal knowledge about what jobs exist and what they do.
How much of your data estate is still undiscovered?
Book a demo and we'll scan a sample of your database and DataStage environment in the first session. Know what you have before you govern it.
What gets discovered
PipelineX Data Catalog: What Gets Discovered
PipelineX automatically crawls and catalogs assets across five platform tiers — from on-premises databases and DataStage through to modern cloud data platforms — with no manual configuration of individual assets.
Relational Databases
Native discovery across SQL Server, Oracle, PostgreSQL, MySQL, and more extracts tables, views, stored procedures, packages, and routines. Column-level metadata — data types, constraints, nullability, row counts, and sample values — is captured alongside technical descriptions pulled from each database's system catalog.
- SQL Server, Oracle, PostgreSQL, MySQL, and more
- Tables, views, materialized views
- Stored procedures and packages
- Column metadata, constraints, and lineage
IBM DataStage
DataStage DSX export parsing extracts all job metadata — server jobs, parallel jobs, shared containers, job sequences — plus connection definitions, transformation stage configurations, and source-to-target column mappings for column-level lineage in the catalog.
- Server and parallel jobs
- Job sequences and shared containers
- Connection and connector metadata
- Stage transformations and lineage
Databricks
PipelineX connects to Databricks Unity Catalog to discover all catalogs, schemas, tables, views, and Delta Live Table pipelines. Databricks notebook assets are also cataloged, with lineage captured from Unity Catalog lineage events to trace data flows end-to-end.
- Unity Catalog: catalogs, schemas, tables
- Delta Live Table pipelines
- Databricks notebooks
- Lineage from Unity Catalog events
Microsoft Fabric
PipelineX discovers Fabric workspaces, lakehouses, warehouses, Data Factory pipelines, Dataflow Gen2 definitions, and Power BI semantic models — providing full coverage of the Fabric OneLake ecosystem including lineage between Fabric objects.
- Workspaces, lakehouses, warehouses
- Data Factory and Dataflow Gen2
- Power BI datasets and reports
- OneLake file and table metadata
Snowflake
Snowflake discovery covers all databases, schemas, tables, views, external tables, tasks, and streams. Dynamic Table definitions and Snowpark procedure assets are also cataloged with technical metadata and lineage from Snowflake's access history and query lineage APIs.
- Databases, schemas, tables, views
- Tasks, streams, dynamic tables
- Snowpark procedures and functions
- Lineage from query history
Cross-Platform Lineage Graph
All discovered assets are connected into a single cross-platform lineage graph — so a database source table can be followed through a DataStage job, into a Databricks Delta table, through a Snowflake transformation, all the way to a Power BI report in one continuous, queryable lineage view.
- End-to-end cross-platform lineage
- Column-level tracing
- Impact analysis and dependency view
- Exportable provenance reports
Intelligent governance
AI-Powered Data Classification
PipelineX applies AI-powered classification to every discovered asset — automatically identifying sensitive data, assigning business concepts, and linking catalog entries to the business glossary without manual effort.
Automatic PII Detection
PipelineX's classification engine scans column names, data types, and sample values across your database tables, DataStage metadata, Databricks tables, Fabric warehouses, and Snowflake schemas to automatically identify columns likely to contain personal data — names, email addresses, national identifiers, financial account numbers, health data fields, and location data. Identified PII columns are flagged for governance review and tracked in lineage for GDPR compliance.
Business Concept Tagging
PipelineX analyses column names, table names, and data patterns to suggest business concept tags — Customer, Product, Transaction, Risk Exposure, Regulatory Reporting. Auto-suggested tags are presented to data stewards for confirmation rather than applied automatically, ensuring business accuracy while eliminating the cold-start problem of empty catalog entries across a newly discovered estate.
Sensitivity Scoring
Every cataloged table and column receives an automated sensitivity score — Public, Internal, Confidential, Highly Restricted — based on detected data types, classification tags, and lineage connections to regulated data. Sensitivity scores drive access control recommendations and compliance reporting, giving data governance teams a quantified risk view of the entire data estate without manual assessment of individual assets.
Business Glossary Auto-Linking
PipelineX maintains a business glossary of enterprise data terms and automatically links cataloged columns and tables to the glossary terms they represent. When a new database table or DataStage job is discovered, PipelineX suggests glossary term links based on naming conventions and context — so catalog entries arrive with business meaning attached, not as blank technical metadata records requiring steward annotation from scratch.
Why PipelineX
PipelineX vs Other Data Catalog Tools
Few data catalogs cover legacy ETL (IBM DataStage) alongside cloud-native platforms. PipelineX does both — providing the unified cross-platform catalog that hybrid enterprises actually need.
| Capability | PipelineX | Cloud-suite catalog | Lakehouse-native catalog | Standalone governance catalog |
|---|---|---|---|---|
| IBM DataStage discovery | Partial | |||
| Database native discovery (SQL Server, Oracle, PostgreSQL & more) | Limited | |||
| Databricks Unity Catalog integration | Limited | Via connector | ||
| Microsoft Fabric discovery | Via connector | |||
| Snowflake discovery | Via connector | Via connector | ||
| Cross-platform lineage graph | Azure-only | Databricks-only | Via connectors | |
| AI-powered PII classification | Limited | Add-on module |
Key advantage: PipelineX provides cross-platform coverage including legacy ETL (IBM DataStage) that cloud-suite, lakehouse-native, and standalone governance catalogs cannot discover natively.
Catalog & AI
A Catalog Turns Migrated Data Into AI-Ready Infrastructure
AI initiatives stall when teams cannot find, trust, or understand the data they need. A data catalog is what turns a migrated estate into AI-ready data infrastructure — making every dataset discoverable, classified, and documented so data scientists and AI agents can use it safely.
Enterprise AI data readiness needs a governed catalog
Enterprise AI data readiness is not just about having data in the cloud — it is about knowing what that data means, who owns it, how sensitive it is, and whether it can be used for a given purpose. PipelineX catalogs every asset across DataStage, databases, Databricks, Fabric, and Snowflake, with business glossary terms, classification, and stewardship workflows so AI projects start from a trusted, well-described foundation rather than an unlabeled data lake.
Combined with column-level lineage, the catalog lets you govern which datasets are approved for AI and trace any model input back to its source. For the broader case, read why data migration comes before AI.
Common questions
Data Catalog FAQs
What is an enterprise data catalog?
An enterprise data catalog is a searchable, governed inventory of all data assets in an organisation — tables, views, pipelines, reports, schemas, and business terms — enriched with business descriptions, ownership metadata, data classifications, and lineage. It enables data engineers to find datasets, analysts to understand data context, and governance teams to document and enforce data policies. A modern data catalog includes automated discovery, AI-powered classification, and lineage integration so the catalog stays current as the data estate evolves — rather than becoming stale documentation within weeks of publication.
How is a data catalog different from a data lineage tool?
A data catalog is a searchable inventory of data assets — what exists, who owns it, what it means, and how it is classified. A data lineage tool documents how data flows between those assets — which pipelines transform which columns from which sources to which targets. PipelineX provides both capabilities in an integrated platform: the catalog for asset discovery and governance, and lineage for understanding data flows and impact analysis. Together they give data governance teams the complete picture: what data exists and how it moves through the organisation.
Can PipelineX catalog Oracle, SQL Server, and other databases?
Yes. PipelineX includes native database discovery across SQL Server, Oracle, PostgreSQL, MySQL, and more — automatically crawling database schemas to extract tables, views, stored procedures, packages, and routines. Objects are cataloged with technical metadata — column names, data types, constraints, row counts, and sample values — and can be enriched with business descriptions, owner assignments, and sensitivity classifications. Database lineage is also extracted, connecting source objects to downstream DataStage jobs and cloud platform tables in the cross-platform lineage graph.
How does PipelineX handle DataStage metadata during catalog migration?
PipelineX parses IBM DataStage DSX exports to extract all job metadata — job names, descriptions, stage configurations, connection definitions, and source-to-target column mappings. This metadata is imported into the PipelineX catalog where it becomes searchable and governable alongside cloud platform assets. During migration to Databricks, Fabric, or Snowflake, DataStage metadata is mapped to the equivalent cloud-native pipeline assets so cataloged information is preserved — not discarded — when DataStage jobs are decommissioned. The organisation exits the migration programme with a fully populated catalog.
Does PipelineX replace the catalog built into my cloud platform?
No — it is complementary. Platform-native catalogs provide governance within their own platform (one cloud suite, or one lakehouse). PipelineX fills the gap they cannot cover: cross-platform discovery that includes legacy on-premises systems such as Oracle and IBM DataStage. For organisations running a hybrid estate, PipelineX provides the unified catalog that spans legacy and cloud, while each platform's native catalog continues to handle platform-specific governance and access control within its own environment.
How does an enterprise data catalog help with compliance?
An enterprise data catalog supports compliance by documenting where sensitive data exists, who is responsible for it, how it is classified, and how it flows through the organisation. PipelineX's AI-powered PII detection automatically identifies personal data fields across your databases, DataStage, Databricks, Fabric, and Snowflake assets. This classification layer — combined with column-level lineage — enables organisations to respond to GDPR data subject access requests, produce BCBS 239 lineage attestations, and provide HIPAA audit evidence without manual investigation across multiple systems.
Related solutions
Explore Related Capabilities
Data Lineage
The data catalog is the inventory — data lineage shows how everything is connected. Together they provide complete data governance across your entire estate.
Enterprise Data LineageDataStage Migration
The PipelineX catalog provides the inventory foundation for DataStage migration wave planning — and preserves DataStage metadata as jobs move to cloud-native platforms.
DataStage MigrationETL Modernization
A populated data catalog is the deliverable that proves an ETL modernization programme succeeded — PipelineX builds it as part of the migration, not after.
ETL ModernizationSee what we find
Catalog Your Entire Data Estate with PipelineX
Book a catalog demo and we'll show PipelineX discovering database schemas, DataStage jobs, and cloud platform assets — classified, linked, and lineage-connected — in a single session.