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Unified Data Catalog & Governance.
‍Across Every Engine, Every Environment

Enforce policies across your data stack — so every decision is backed by trust.

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See How It Works

TRUSTED BY ENTERPRISE DATA TEAMS WORLDWIDE

What Changes When You Have One Governed Catalog

Before xLake

"Why did this pipeline fail?" 4 tools. 45 minutes. Every time.

You have a question. The engineer has a queue. Decision made before the answer arrives.

Half your week on checks that should be automated. Audits take days - the trail lives in three systems.

Question Monday. Analyst's slide Thursday. Call already made — on stale data.

With xLake

One policy layer governs every engine, enforced at workload level

Ask in plain English. Get live results. No ticket, no wait.

Anomalies flagged before you find them. 23 weeks of policy config → 46 hours. Audit PDF in one click.

Join any active incident - get an executive view while engineering works the trace.

How It Works

xLake's catalog layer sits between your engines and your data. Every read, write, and execute operation passes through it. Governance applies before anything moves.

Asset Registration

Every asset touched by Spark, Trino, Jupyter, or Airflow is registered in the governed catalog. Tables, notebooks, pipelines, AI-generated jobs.

Policy Definition

Java or Python — complete and executable. Not a scaffold. Not a stub.

Real-Time Enforcement

Live cluster config, data store connectivity (ODP, S3, HDFS, Vast), scheduling dependencies — resolved at generation time, not at runtime.

Automatic Lineage Capture

Metadata, dependency map, audit trail — committed automatically. No manual tagging. No separate governance step.

Continuous Audit Logging

Visible in Platform Pulse from the moment it's registered. Observability starts on day one. Plain-language input to monitored, production-ready pipeline — without leaving XDP.

One Catalog Layer.
Zero Uncontrolled Engine Surfaces.

xLake integrates a single governed metadata layer across every workload in your stack.

Every data asset — tables, models, pipelines, notebooks — registered, classified, and subject to consistent access controls, regardless of engine.

Job-level metadata registration and access enforcement

Runs logged, assets accessed under full access control

xLake Governed Catalog Layer

Single policy definition. Universal enforcement.
Policy applies identically
across all engines

Federated query governance with no policy bypass

DAG-level audit trails captured per execution

Active Governance — Not a Metadata Warehouse

Access controls apply in real time across read, write, and execute operations at the workload level.
When a data asset changes, policies update everywhere, immediately, without manual intervention.

Governance Dimension

What xLake Does

Access Control

Read/write/execute policies enforced consistently across all four engines

Federated Query Governance

Cross-catalog Trino queries subject to the same policies as native Spark workloads — no policy bypass through query federation

Audit Trails

Every Spark job, Trino query, notebook run, and Airflow DAG generates a workload-level access log

Lineage

End-to-end lineage tracked automatically from ingestion through transformation to consumption

AI-Generated Code

Jobs generated by xLake's AI Engineer are registered directly into the governed catalog — same metadata, lineage, and access controls as human-authored code

Hybrid-Native.
Not Stack-Native.

Identical governance policies enforce across on-premises Kubernetes clusters — EKS, AKS, GKE, bare-metal — and cloud environments, in a single deployment.