Your data has
an OS now.
DMOS is the operating system for your entire data stack. It senses anomalies, compiles pipelines, enforces quality, and manages the full data lifecycle — across every warehouse, lake, and storage layer you already use.
An operating system for your entire data stack
Linux doesn't care what hardware you run. DMOS doesn't care where your data lives. It sits above your existing warehouse, lake, or database and manages the full lifecycle — ingestion, transformation, quality, catalog, lineage, governance, security, and observability — as one coherent system.
DMOS deploys as a complete per-tenant instance — like Snowflake provisions a warehouse per customer. Your data never leaves your infrastructure. Zero vendor lock-in on the storage layer.
Keep Snowflake, Databricks, Postgres, S3, Iceberg — DMOS works with all of them
One system for the entire data lifecycle instead of 6-8 point solutions stitched together
The engine senses, reasons, and acts autonomously within policy boundaries — no alert fatigue
The full data lifecycle, one platform
DMOS covers every domain in the DMBOK — from ingestion to governance, quality to security, observability to cost management. Not 8 point solutions glued together. One coherent operating system.
Data Integration
Connect any source — databases, APIs, files, streams. AI maps fields to your catalog. Schema evolution handled before it breaks anything downstream.
Transformation & Pipelines
Describe pipelines in plain English. PDL compiles them to typed, lineage-proven SQL. Column references verified against the live catalog at compile time.
Data Quality
Quality checks are first-class language constructs, not YAML configs bolted on after. AI generates expectation suites from data profiles. Anomaly root cause analysis in plain English.
Metadata & Catalog
Auto-populated from every pipeline run. Natural language search across all assets. AI writes documentation. Column-level lineage traced from source to report.
Observability
9 cognitive senses monitor freshness, volume, schema drift, meaning shift, and more. The engine detects anomalies before your stakeholders notice. AI explains root cause.
Data Governance
Policy-as-code governance. PII detection and classification. Data ownership and stewardship. Compliance evidence generated from the audit log — GDPR, SOC2, HIPAA.
Security & Access
Role-based access control, row-level security, encrypted credential storage, SSO/SAML. Per-domain compute isolation. Per-tenant deployment with zero cross-tenant data paths.
DataOps
Every pipeline is a versioned, reviewable artifact. AI reviews changes for type safety, quality coverage, and lineage impact. CI/CD for data with promotion gates.
Data Architecture
Mesh Grid / Mesh / Domain / Asset hierarchy. Data products with SLAs. Architecture-as-code. Blast radius analysis before any change reaches production.
Reference & Master Data
Golden record management. Cross-source entity resolution. Consistency sensing detects when the same entity has conflicting values across systems.
FinOps & Cost Management
Per-domain and per-pipeline cost attribution. Compute resource quotas. Query cost estimation before execution. Usage dashboards for data platform ROI.
It doesn't alert you. It handles it.
Nine sensors watch every dataset continuously. The engine reasons about anomalies with explicit confidence, acts within your policy boundaries, and remembers every episode. You read the resolution report at 9 a.m. instead of debugging at 3 a.m.
9 sensors detect anomalies across freshness, volume, schema, semantics, and more
Rule engine + optional LLM scores confidence and proposes typed actions
Policy gates enforce boundaries — the engine self-heals within rules you set
Every incident becomes an episode with causal graph, resolution, and reasoning
Nine cognitive senses
Confidence bands — the engine knows what it doesn't know
Every autonomous action is gated by explicit confidence. Low confidence means log-only. High confidence means self-heal. Destructive actions always require human approval regardless of confidence.
Log only. No autonomous action.
Notify operator. LLM may refine.
Autonomous low-risk actions.
Autonomous non-destructive actions.
Episodic memory
Every incident, decision, and autonomous action is recorded with confidence levels, reasoning chain, and causal graph — a flight recorder for your data platform.
English in. Proven pipelines out.
Describe what you need. The compiler verifies it. Your warehouse executes it. The engine watches it. Four steps — from intent to autonomous operation.
Describe
Tell DMOS what you need in plain English. "Build a daily revenue pipeline from our orders table with a freshness SLA of 6 hours."
Tell DMOS what you need in plain English. "Build a daily revenue pipeline from our orders table with a freshness SLA of 6 hours."
Compile
AI generates PDL — a typed pipeline language. The compiler verifies types, resolves catalog references, proves lineage, and catches errors before execution.
AI generates PDL — a typed pipeline language. The compiler verifies types, resolves catalog references, proves lineage, and catches errors before execution.
Execute
The compiled pipeline runs on your existing storage — Snowflake, Databricks, PostgreSQL, DuckDB, or S3. DMOS pushes compute to where your data lives.
The compiled pipeline runs on your existing storage — Snowflake, Databricks, PostgreSQL, DuckDB, or S3. DMOS pushes compute to where your data lives.
Sense
From this moment on, the cognitive engine monitors your pipeline's data. Freshness, volume, schema changes, distribution shifts — continuously, autonomously.
From this moment on, the cognitive engine monitors your pipeline's data. Freshness, volume, schema changes, distribution shifts — continuously, autonomously.
Every seat at the table, one platform
Delete your on-call anxiety.
60% of your week is debugging, not building — tracing schema changes through undocumented hops
The 3 a.m. Slack ping. Quality tools detect; nothing acts. Every alert is your problem to triage.
AI tools that lie confidently — LLM-generated SQL looks right, runs, and silently produces wrong numbers
Lineage is a compiler artifact — it cannot drift from reality because it IS the pipeline
The engine quarantines bad data, pauses downstream jobs, and writes a resolution episode. You read it at 9 a.m.
Natural language goes through a real compiler — lexer, parser, type checker, codegen. If it compiles, it's correct.
“Stop being the alerting system.”
Keep them. That's the point.
DMOS is not in the stack. It's above the stack. Every tool below is an excellent place to store, compute, or observe data. DMOS is the operating system that manages what happens across all of them — no migration, no rip-and-replace.
A place your data lives. DMOS manages data wherever it lives — including inside Snowflake.
A powerful compute and lakehouse engine. DMOS is the cognitive layer that keeps everything running on it healthy and governed.
Templates SQL and suggests structure. DMOS compiles pipelines through a type checker with proven lineage — and covers the ten DMBOK areas dbt never touches.
Observability tools tell you what's wrong. DMOS senses, reasons, and acts — detection is a sensor input, not the product.
Documents governance policy. DMOS executes it — policies become runtime boundaries the engine enforces, not workflows people follow.
DMOS works with the infrastructure you already invested in. Your data never moves. Your warehouse stays. You gain management, governance, and cognitive sensing — across all of them, from one system.
See how DMOS fits your stackA Rust engine. A real compiler. Your boundary.
Per-customer isolated deployments, a cognitive core built for continuous operation, and a pipeline compiler with formal type checking.
Per-customer isolation
Every customer gets their own DMOS instance in a dedicated K8s namespace — like Snowflake deploys a warehouse per account.
Real compiler, not AI vibes
Lexer, parser, name resolver, type checker, codegen. If a pipeline compiles, its types check and its lineage is proven. No hallucinations ship.
Rust cognitive core
18-crate workspace with 317+ tests. Memory-safe, zero-cost abstractions, sub-millisecond sense cycles. Built for continuous autonomous operation.
Your data estate is about to
get an operating system.
DMOS is rolling out as isolated per-customer deployments. Join the waitlist to get early access — your data stays where it is, and DMOS manages all of it as one coherent system.
No spam. No vendor lock-in. Building in public at github.com/dataastra