Lugal — Managed Data Pipeline Platform

Submit Your Pipeline.
We Handle Everything Else.

Lugal runs large-scale, long-running data pipelines on cloud compute you never have to manage. Send us a config, get back results and a transparent bill — no orphaned instances, no surprise costs.

From Config to Results — No DevOps Required

Lugal abstracts away every layer of infrastructure so researchers and data teams can focus entirely on their science.

01

Submit

Upload your data to S3 and send a pipeline config via API or CLI. Lugal validates the spec and queues it immediately.

02

Execute

Jobs launch in sequence on dedicated cloud compute. Each job passes its output to the next — no manual handoffs.

03

Monitor

Heartbeats and structured logs flow back in real time. If a job fails, Lugal records the halt point and waits for your instruction.

04

Deliver

Your output lands in S3. Compute shuts down the moment the pipeline completes. You receive results and an itemised bill.

Built for Data-Heavy, Long-Running Work

Lugal is purpose-built for pipelines that run for hours or days on large datasets — not quick web requests or real-time streams.

Bioinformatics & Genomics

Run sequencing, variant calling, and multi-step analysis workflows across large datasets. Each tool in your pipeline gets its own isolated job with defined inputs, outputs, and retry behaviour.

Research & Data Science

Run simulations, parameter sweeps, and batch analyses without provisioning a single server. Submit once, get reproducible results, rerun months later with the same environment.

Regulated & Compliance-Sensitive Work

Every resource is tagged to a client, project, and pipeline run. Audit trails are a first-class output — not an afterthought — making compliance conversations straightforward.

No Free Stuff. No Surprises.

Lugal was designed around a single principle: every unit of compute must be traceable to a billable job. There are no silent retries, no orphaned instances, and no untracked resources left running after a pipeline finishes.

Every AWS resource created is recorded before it starts
Resources are tagged to client → project → pipeline → job
Cleanup runs automatically on success or failure
Billing exports are a first-class output, not a separate report
Restart from the failed job — not from the beginning

What You Get at the End of Every Run

📦
Pipeline Outputs
Final results in your S3 bucket — files, reports, or whatever your jobs produce.
📋
Full Execution Record
A plan.json capturing every task, its inputs, outputs, timing, and exit state.
📊
Itemised Bill
Every AWS cost driver mapped to your pipeline, job, and task. No line item goes unexplained.
🔍
Logs & Audit Trail
Structured logs and heartbeat history for every job, queryable long after the compute is gone.

Pipelines Are Simple to Define

A Lugal pipeline is a JSON config — a named list of jobs, each with its own tools, inputs, and outputs. No YAML sprawl, no DAG frameworks to learn.

Pipeline

The top-level unit. Has a name, a client, and an ordered list of jobs. Submitted once via API.

  • Client & project attribution
  • Ordered job sequence
  • Resource quotas & retry policy
Job

A single unit of work that runs on its own EC2 instance. Reads from S3, writes back to S3.

  • Dedicated compute per job
  • Defined inputs & outputs
  • Configurable parallelism
Task

The individual steps within a job — your actual tools and scripts, run in sequence.

  • Your code, your tools
  • Automatic input/output wiring
  • Per-task retry & failure handling

Ready to Run Your First Pipeline?

Whether you're processing genomics data, running simulations, or building a multi-step analytics workflow — Lugal handles the infrastructure so you don't have to.