Sidra is a metadata-driven data platform built on Azure and Databricks/OneLake, designed to accelerate the creation of data products, unify data architecture, and simplify the journey from raw ingestion to actionable insight. Whether you're building traditional BI solutions or advanced AI models, Sidra gives your teams the tools and structure to deliver faster—with full governance and security by design.

Sidra enables organizations to build secure, modular, and production-ready data products on top of centralized infrastructure, without reinventing the wheel.

Sidra features:

• Accelerated Ingestion & ELT Pipelines:
  Automatically generate metadata-driven pipelines to ingest structured, semi-structured, and unstructured data from hundreds of sources, including SQL Server, Oracle, Snowflake, APIs, and more—no need for manual development or infrastructure scripting.

• Data Products Architecture:
  Native support for scalable, domain-oriented Data Product design—core to enabling Data Mesh adoption. Create and manage isolated, versioned, and governed data products in minutes using standardized templates.

• Enterprise-Ready Services:
  Built-in services like Supervisor (for deployment & updates), Authentication & Authorization (Keycloak + Balea), Data Quality (powered by Great Expectations), Data Catalog, and API Builder (auto-generating REST and GraphQL APIs).

• AI-Powered Metadata Enrichment:
  Automatically generate smart, context-aware descriptions for data entities and attributes using AI—improving discoverability and documentation.

• Flexible Storage and Compute:
  Use Azure Data Lake and Databricks, or combine with Microsoft Fabric and OneLake. Sidra’s architecture supports multiple DSUs across regions, ensuring compliance and cost control.

• Security & Compliance First:
  All resources are provisioned with tagging, RBAC, private endpoints, VNET support, audit trails, and encryption by default—built for enterprise security and regulatory alignment.

• Built-in DevOps & CI/CD:
  Fully infrastructure-as-code deployments with automation of pipelines, notebooks, metadata, and models—shortening the path from dev to prod.