Modern ModelOps

Integrating and extending your tools and infrastrucure

A thin orchestration layer that creates a self-describing, self-documenting and stateful analytics environment.

Solution architecture

Self-describing

Built around a semantic data model that catalogues and describes every asset, process, and calculation.

Self-documenting

Time-consistent action and update recording — a complete version history of the whole platform.

Stateful

Every version of every asset and every prior state of the platform is available to each user, all the time.

The trac ecosystem

See how trac integrates your exisitng tools and infrastructure.

Production deployment

Flexible integration options

Integrating seamlessly with most modern data technologies, code repositories and development tools.

An append only data model ensures job repeatability. For cloud deployments, primary storgage uses regular bucketd (S3, GCS or Azure Blob).

Kubernetes with single-node and distributed (Spark) for cloud deployments - other compute services are available for on-premises.

Models are stored in a repository and accessed dynamically at runtime. Git and Nexus are supported natively, custom repositories are accommodated with plug-ins.

Deploy the trac runtime (python) to your IDE and build models that can be deployed to production with no code modification.

Connectivity is available for most common technologies including; object storage, file-like locations, databases, and SQL-like locations.

Plug into your SSO mechanism (Azue AD, SAML or OpenID) with roles assigned in the AD and fine-grain permissions configured in trac.

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Our philosophy

Built on open-source foundations

We publish trac's core analytic services as an open-source project (TRAC D.A.P.) via FINOS. The trac model runtime is also available to download from pypi.org.

Explore open-source

Download the free desktop version and start exploring today.

Try Community Desktop

Universal pattern

Development, desktop, or production system

Use trac in your IDE, as a desktop tool, or to run multiple production workdloads in the cloud or on-premises.

  1. 01 - In an IDE

    Use the trac model runtime in your development environment to build portable, production-strength models.

  2. 02 - On your desktop

    A single-user sandbox deployment - ideal for exploration or to replace EUCs and non-production workflows.

  3. 03 - In the cloud

    For AWS, Azure and GCP we offer a simple marketplace solution or a Kubernetes pattern, for production workloads.

  4. 04 - On-premises

    Various on-premises deployment patterns can also be supported depending on your infrastructure availability and needs.