Three decisions, made well, instead of eight you have to make yourself.
Instead of stitching eight tools together and calling the result a platform, we made three architectural decisions once, and built an integrated platform that shares them.
Six layers. One stack. One set of policies.
Sources feed ingestion; ingestion feeds processing; processing feeds intelligence and delivery. SMART governance and the Data Catalog span every layer from day one, not bolted on at audit time.
Every layer shares the same RBAC, the same observability, and the same audit trail, so lineage survives contact with production.
Unified by architecture, not integration.
One data model, one governance layer, one RBAC. An integrated platform built together instead of stitched by your team.
- An integrated platform on a single Spark + Kubernetes foundation.
- One RBAC model, platform, module, row, and column.
- One catalog. Lineage across every transformation, automatically.
- Cloud-agnostic: AWS, Azure, GCP, or on-prem; data-plane / control-plane split.
AI that ships with the platform, not on top of it.
ML Studio, Forecaster, and Anomaly Detector cover the full ML lifecycle. A growing library of agents lets teams converse with the platform in plain English.
- Talk to Your Data: natural-language queries over SQL, NoSQL, S3, APIs.
- ETL/ELT Designer agent turns requirements into deployment-ready pipelines.
- 25+ time-series algorithms; one-click model deployment as REST APIs.
Governed, observed, and self-healing by default.
The SMART framework, SLA, Monitoring, Actions, Rules, Traceability, is embedded in every module. Sherlock closes the loop with autonomous remediation.
- SLA breach alerts before the breach, not after.
- Sherlock diagnoses incidents across live telemetry and historical failures.
- Automated PII tagging and classification in the catalog.
- Autonomous root-cause analysis with validated remediation.
- Cross-module audit trail for GDPR, HIPAA, and SOX reporting.
Converse with the platform. A growing library of agents.
The agents aren't a separate product. They reach into every module, ingestion, transformation, ML, operations, and take instructions in plain English. New agents ship with the platform regularly.
- Talk to Your Data: Plain-English queries over SQL, NoSQL, S3, Cassandra, and APIs.
- ETL/ELT Designer: Describe the requirement, and the agent ships a deployment-ready pipeline.
- Spark Summarizer: Turns verbose Spark logs into "what ran, failed, was slow, fix this."
- ProcBot Designer: Describe a process, and the agent generates the working script in bash, Python, Terraform, or Ansible.
- Sherlock: Autonomous agents help with not only problem discovery but throughout the process from problem detection to auto-remediation and closure.
- DataOps AI: Ask about pipeline health and SLAs; answers come from live telemetry.
- AI Governance & Intelligence: AI agents continuously enrich metadata, classify sensitive data, monitor compliance, and generate governance insights across enterprise data assets.
- Data Exploration AI: Describe the requirements in natural language, and the agent generates the transformation code behind the scenes to produce the output.
SMART, the framework embedded in every module.
Governance isn't a separate tool or a separate project. The same five primitives run inside every module of the platform. That's the only way compliance reporting becomes a report instead of a project.
- SSLA
Per-pipeline thresholds; alerted before breach, not after.
- MMonitoring
Continuous lifecycle monitoring across executions.
- AActions
Automated responses: notify, retry, escalate, reroute.
- RRules
Business and technical rules enforced at the platform level.
- TTraceability
Cross-module lineage and audit trail, source to consumer.
Want the technical walk-through?
We'll show you the modules, the SMART framework, and how lineage actually works end to end.