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Multi-Engine Control Plane. Run Every Job on a Single Dashboard.

xLake consolidates Spark, Trino, and Jupyter under a single control plane — on your Kubernetes cluster, inside your VPC, on your terms.

TRUSTED BY ENTERPRISE DATA TEAMS WORLDWIDE

  Every Job Type. One Dashboard.

xlake runs Spark Java, Spark Python, Notebook, and Trino jobs from a single Jobs dashboard — not per-engine consoles bolted together.

What You See
Why It Matters
Schedule and cluster assignment per job
No manual cross-referencing between schedulers and configs
Success rate across all engines
Catch degrading pipelines before they become incidents
Last five run statuses per job
Diagnose failure patterns without hunting through separate logs
Live status across Spark, Trino, and Jupyter
One source of truth for platform and data engineers
No tool-hopping. No fragmented overhead.

The Problem Data Teams Are Done Tolerating

Spark for batch. Trino for queries. Jupyter for exploration. Three engines, three job UIs, three operational overhead streams — and zero unified visibility.

Engineers context-switch between toolchains to diagnose a single failure
No unified view of job health across engines
Triple the monitoring, alerting, and access-control burden
50%+ cloud markup when a vendor owns the compute

What You Eliminate on Day One

Before xDP
Three separate job management UIs
Per-engine monitoring and alerting
Vendor-managed compute with PaaS markup
Infrastructure team excluded from data tooling
Lock-in to proprietary runtimes
After xDP
One unified Jobs dashboard
Single observability surface via Pulse on Kubernetes
Compute you provision and control
Platform and data teams share one control plane
Any K8s 1.19+ distribution, no proprietary dependencies

How It Works

xLake installs into your existing Kubernetes namespace. No proprietary runtime. No infrastructure replacement. No external control plane.

1
Deploy onto your cluster

Standard Kubernetes constructs only — namespaces, resource quotas, node pools. Nothing proprietary.

2
Submit a Single SQL Query

Write standard SQL. xLake's Trino engine builds an optimized distributed execution plan across every registered catalog.

3
Policy Enforced Before Execution

Access controls and catalog visibility are evaluated at the query layer — natively — before a single byte is read.

4
Execution on Your Infrastructure

Queries run on your Kubernetes clusters — EKS, AKS, GKE, or on-prem K8s 1.20+. You control resource limits and cost ceilings.

5
Results Return. Nothing Replicated.

Results go directly to the requesting system. No intermediate copies. No sync jobs. No replication pipelines.

6
Every Execution Is Observable

Query history and execution logs capture which catalogs and nodes handled each stage. Deeper observability added via Pulse observability integration.

Your Kubernetes. Your Compute. No Vendor Markup.

xLake runs on any Kubernetes 1.19+ distribution - EKS, AKS, GKE, or on-premises.
No proprietary runtime. No custom cloud dependency.
Clusters deploy inside your VPC and your Kubernetes namespace. Data never crosses your infrastructure boundary. 

Built for Platform Engineers and Data Teams

Platform Engineers
Work with standard Kubernetes constructs they already understand — namespaces, resource quotas, node pools — no proprietary abstraction layer to learn.
namespaces
node pools
resource quotas
Data Engineers:
Schedule and monitor jobs through a purpose-built UI without needing to understand the underlying K8s mechanics. Focus on pipelines, not infrastructure
Jobs UI
Scheduling
Monitoring
Both Teams. One View.
Share the same dashboard, the same cluster view, the same source of truth. No more coordination overhead across separate tooling.
Shared Dashboard
Unified View

One Control Plane. Every Workload Type.

Every cost driver that legacy platforms obscure or ignore — surfaced and resolved.

Runs Spark jobs
Runs Trino queries
Runs Jupyter notebooks
Deploys on your Kubernetes cluster
Customer-owned compute
Unified job dashboard across all engines
No proprietary runtime dependencies
Trino-Only
Platforms
Spark-Centric Managed Platforms
xLake

See Spark, Trino, and Jupyter running from a single xLake control plane on your target Kubernetes distribution.

Frequently Asked Questions

Q1. Do I need to know Spark, Java, or Python to use xLake?

No. xLake is designed so that engineers and data practitioners can describe pipeline intent in plain language — what data to move, how to transform it, and where it should land. xLake handles all code generation. Knowledge of Spark syntax or orchestration frameworks is not required to author a production-ready pipeline.

Q2. What data stores and environments does xLake support?

Agents detect and resolve issues proactively, maintaining SLAs even when pipelines fail.

Q3. How does xLake ensure the generated pipeline is actually production-ready?

Yes—trust agents validate data early in the workflow, reducing errors downstream.

Q4: What happens to lineage and governance after a pipeline is generated?

ADM works with Airflow, Snowflake, Databricks, and more—enhancing your stack without disruption.

Q5: Can xLake generate both Java and Python Spark jobs?

Governance agents enforce policy and traceability across workflows—supporting GDPR, HIPAA, ESG, and more.

Q6: How is xLake different from visual or no-code pipeline builders?

Customers typically see 30–50% faster workflows and 80% fewer quality incidents.

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