PatternOps
The enterprise data engineering operating system — define what your pipelines do, not how
PatternOps decouples pipeline intent from execution technology, so you build once and run anywhere — Spark, Flink, Snowflake, Databricks — without vendor lock-in.
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What PatternOps does
PatternOps is bigspark's data engineering operating system. You describe what a pipeline must achieve in a simple, technology-free definition; interchangeable providers decide how it runs — with governance, observability and AI integration built in from the start.
Technology-free pipelines
Define pipelines in simple YAML or JSON. No Spark configs, no Airflow DAGs, no cloud-specific plumbing leaking into your logic.
Swap engines without rewrites
Interchangeable providers let you move between Spark, Flink, Snowflake and Databricks without touching your pipeline definitions.
Observable by design
OpenTelemetry traces and structured logging across every stage, so lineage, metrics and quality are there from day one.
AI-native
Model Context Protocol (MCP) tools expose the whole platform to AI agents, with built-in safety classification.
How PatternOps works
Define
Describe pipeline intent (acquire, validate, transform, publish) in a portable definition using YAML, JSON or HOCON.
Map to capabilities
Stable capability contracts — source acquisition, transformation, storage, security, data quality, observability — describe what must happen.
Run on any provider
Pluggable providers execute the work on your chosen platform, with dual-mode transformations (Spark + SQL) selected automatically.
Govern & observe
A policy engine, multi-tenancy and full telemetry keep every run governed, isolated and auditable.
Built to end vendor lock-in
Data platforms change; your pipelines shouldn't have to. By separating intent from execution, PatternOps lets enterprises migrate between engines, standardise observability across the estate, and open their data platform to AI agents — without the rewrite tax that usually comes with change.
Build once. Run anywhere.
Most data pipelines are welded to the technology that runs them — a tangle of Spark configs, Airflow operators and cloud-specific constructs. Change the platform and you rewrite everything. PatternOps breaks that coupling: you define what a pipeline must do, and interchangeable providers determine how it runs.
The result is a portable, governed, observable data engineering layer that moves with you as your platform evolves.
Key principles
- Capability-driven — Define what must happen; providers determine how.
- Technology-free pipelines — No Spark configs, no Airflow DAGs, no cloud-specific constructs in your pipeline definitions.
- Multi-platform portable — Dual-mode transformations (Spark + SQL) with automatic execution-strategy selection.
- Observable by design — OpenTelemetry traces, structured JSON logs and end-to-end lineage.
- Multi-tenant by default — Tenancy and namespace isolation at every layer.
- AI-native — MCP tools with safety classification for AI-agent integration.
Explore the framework
PatternOps ships with Apache Spark and Apache Flink providers out of the box, 20+ capability contracts, and an MCP layer that makes the whole platform available to AI agents.
Ready to get started?
Get in touch with the bigspark team to arrange a guided walkthrough.
Awards and Honours
