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The Sidra Approach: Domain-Oriented Data Enablement Built on a Governed Data Lake

In today’s data-driven organizations, the question isn’t whether to centralize data—it’s how to do it in a way that scales, secures, and empowers teams without creating bottlenecks. The Sidra Data & AI Platform is designed to meet that challenge by combining an automated, governed data lake foundation with a domain-oriented model that fosters decentralized innovation.

A Foundation in Data Lake Architecture

Sidra embraces the data lake paradigm not as a complete solution, but as the essential base layer of modern data enablement. All data ingested into Sidra lands in one or multiple Data Storage Units (DSUs)—modular storage environments that can be independently deployed across Azure regions. Each DSU is implemented using Delta Lake format on Azure Data Lake Storage Gen2 (ADLS Gen2).

This automated, technically standardized layer provides:

  • Partitioned, compressed, and indexed storage for efficient access.
  • Automated schema handling (including evolution) and technical validation.
  • Strong governance and access controls aligned to enterprise standards.
  • Region-specific deployments for data sovereignty and compliance.

Critically, the data in a DSU is not a "single source of truth" in the traditional enterprise data warehouse sense. It is not designed to hold final, cross-domain business logic or reconciled KPIs. Instead, it is technically transformed but not business-transformed.

Architecture Overview

Why That Matters

This is one of Sidra’s most important distinctions.

Rather than attempt to centralize and predefine business meaning (e.g., reconciling "Revenue" between Finance and Operations), Sidra defers this to the owners of Data Products. By ingesting data into the DSU in raw-but-standardized form—quickly, painlessly, and without political friction—Sidra avoids the failure patterns of large, centralized data warehouse projects.

Business definitions, logic, KPIs, and models live downstream, where they can be defined, debated, and versioned within context-specific Data Products. This decoupling allows:

  • Rapid ingestion without business alignment delays.
  • Parallel development across teams and departments.
  • Clear ownership of semantics and transformation logic.

In other words: load first, align later—at the domain level.

Domain-Oriented Architecture

Sidra organizes data and its ownership around domains—logical business or functional units that reflect how organizations actually work. Domains can represent departments (e.g., HR, Sales), functions (e.g., BI, ML), or geographies.

Each domain can consume curated slices of the data lake, transforming them into governed, versioned, and productized assets—what we call Data Products.

Sidra includes starter templates for common Data Product scenarios: - BI Dashboards (Power BI or other tools) - ML Projects (Jupyter Notebooks or model training pipelines) - APIs (REST/GraphQL microservices)

Customers can also define and package custom templates. Past implementations have included not just traditional advanced analytics and BI use cases, but even operational web apps, mobile apps, and internal tooling—all modeled and deployed as Sidra Data Products.

These products can expose their data via: - Secure APIs (via API Builder), - Catalogued datasets (via Data Catalog Service), - Direct access for ML/BI pipelines.

Domains own their transformations and are accountable for their logic. Sidra supports this through centralized governance, but distributed autonomy.

ELT by Design

Sidra follows a modern Extract-Load-Transform (ELT) pattern. The "Load" step happens first—clean, structured landing of data into the DSU. Only then do Data Products perform their own transformations.

This model enables: - Reuse of ingested data across multiple domains. - No duplication of ingestion logic across teams. - Flexibility to evolve business rules without re-engineering pipelines.

Because every Data Product is isolated but interoperable, teams can work independently without stepping on each other’s toes—or waiting for centralized modeling efforts.

Integration Toolkits for Complex Sources

Sidra includes two powerful toolkits that enable partners and customers to integrate with custom or niche systems that lack off-the-shelf connectors:

  • Unlimited Connector Toolkit – For building data ingestion plugins tailored to nonstandard or legacy data sources.
  • API Connector Toolkit – For integrating external APIs with Sidra, mapping and ingesting data in a reusable and secure way.

These toolkits extend Sidra’s ingestion architecture and ensure that no system is left behind, even in highly specialized environments.

Modular Platform Services

Sidra provides a suite of integrated services to support data lifecycle automation:

  • Supervisor – Manages Sidra services and deployments.
  • Data Quality Service – Validates and scores data as it lands.
  • API Builder – Automatically exposes data as secure APIs.
  • Data Catalog Service – Indexes and classifies metadata with AI enrichment.
  • Integration Hub – Event-based orchestration and third-party integrations.
  • Authentication & Authorization – Identity and access via Keycloak and Balea.
  • PII Detection – Automatically flags sensitive data for compliance.

Each service is modular, allowing teams to adopt what they need and scale when ready.

Use Cases Across the Data Lifecycle

Sidra supports diverse business and technical use cases:

  • Analytics & Reporting – Trusted data for dashboards and KPIs.
  • AI/ML – Training datasets and feature stores.
  • Operational Intelligence – Real-time APIs for line-of-business systems.

These can be implemented independently or composed across domains to power end-to-end data products.

A Platform Designed to Scale with You

Sidra Data & AI Platform isn't just a data platform—it’s a delivery framework for modern data organizations. By clearly separating technical ingestion from business transformation, Sidra offers fast onboarding, strong governance, and domain-level autonomy.

This means: - No more “eternal” modeling projects. - No more brittle, monolithic pipelines. - No more chasing a mythical single source of truth.

Instead, you get a platform that automates what should be standardized and enables what should be flexible—without compromise.

Learn more about how the Sidra architecture supports your modern data platform strategy in the Sidra Overview.