Acceldata Launches Autonomous Data & AI Platform for Agentic AI Era. Learn More →
Pipeline Orchestration · Kubernetes-Native

Pipeline Orchestration
on Kubernetes - With Full Visibility

Runtime Visibility, Compute Control, fewer SLA Failures for enterprise-grade pipelines on your infrastructure — it's more than just success metrics.

TRUSTED BY ENTERPRISE DATA TEAMS WORLDWIDE
The Problem

The Problem With How Most Teams Run Pipelines Today

Most pipelines fail silently. xLake closes that gap.

False Positives, Invisible Problems

DAGs succeed while Spark jobs silently consume 3× expected resources and downstream tasks queue behind them

No Runtime Visibility

You can't see which queries are creating contention in the 8 PM scheduling window, only that something went wrong afterward

Managed compute lock-in

Platforms that handle orchestration for you do it on their infrastructure, at their pricing, under their constraints

Scaling ceilings

Legacy platforms hit concurrency limits you're now engineering around manually

Fragmented toolchains

Spark, Trino, and Python workloads each need separate tooling, separate logging, separate everything

Compute Control

Run Pipelines on Your Kubernetes. Keep Control of Your Compute.

xLake deploys directly onto your Kubernetes clusters — EKS, AKS, GKE, or on-premises. Your data stays in your environment. Your compute plane stays under your control.

Runtime Observability

See Inside Every Running Pipeline — Not Just Whether It Passed

More than just DAG Success Status. xLake answers questions that actually matter:

Why did that Spark job consume 40% more memory than last Tuesday?
Which Trino queries are creating contention in the 8 PM scheduling window?
What's the per-job resource footprint across every namespace?

Ship Pipelines Faster With AI-Assisted DAG Authoring

Describe a pipeline in plain language.  Upload an existing YAML or SQL spec. xLake generates the DAG. Your engineer reviews, adjusts, and ships.

1
xLake deployed inside your AWS VPC
2
Kubernetes Dashboard and DataPlane Helper ready
3
Control plane traffic secured — data operations never leave your VPC
4
Zero vendor access to your data. Zero egress surprises.
70%
Faster time-to-pipeline.
↓ 3×
Fewer authoring errors
4x
Engineering efficiency

Prevent SLA Failures Before They Happen

Smart job window recommendations:
Analyses historical cluster utilisation and surfaces optimal scheduling windows before you commit a run.
Conflict avoidance logic:
Identifies overlapping workloads across Spark, Trino, and Python jobs and calculates contention risk in advance.

One Control Plane. Every Workload Type.

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

Native Kubernetes deployment on customer infrastructure
Per-job runtime observability at K8s namespace level
AI-assisted DAG generation from natural language / YAML / SQL
Conflict avoidance and smart scheduling windows
Unified orchestration across Spark, Trino, JupyterHub, Python
Data sovereignty — compute stays in your environment
xLake
Standard Orchestrators
Managed Compute Platforms
Partial
Limited
Manual Writing
Spark-primary

Built for teams Running at Enterprise Scale

Where pipeline failure is a business problem. Where a missed SLA has a cost. Where teams are accountable for both reliability and efficiency.

For teams managing dozens of concurrent pipelines across hybrid environments and hitting the limits of what your current orchestrator can observe and legacy platforms can scale — xLake is built for this.

100s
Concurrent pipelines managed
≥99.9%
SLA reliability target
4+
Workload types, one control plane
0
Data leaving your environment

Ready to get started

Explore all the ways to experience Acceldata for yourself.

Expert-led Demos

Get a technical demo with live Q&A from a skilled professional.
Book a Demo

30-Day Free Trial

Experience the power of Data Observability firsthand.
Start Your Trial

Meet with Us

Let our experts help you achieve your data observability goals.
Contact Us