SOT SQL Repository: A Single Source of Truth

• SOT SQL repository is a governed system that consolidates raw data into canonical, semantically enriched SQL views for accurate analytics.
• It integrates a formal Semantic SQL Transducer and CI/CD controls to ensure lossless mapping, consistency, and auditability.
• The system supports scalable, automated data operations while enforcing strict governance and bridging business-technical needs.

A Single Source of Truth (SOT) SQL repository is a rigorously governed system that consolidates, semantically models, and materializes all relevant data into canonical, lossless, and auditable SQL views or tables that serve as the authoritative basis for downstream analytics and operations. By uniting strong mathematical foundations in semantic modeling—such as the Semantic SQL Transducer (SST)—with robust DataOps-driven CI/CD pipelines and automated validation, the SOT SQL repository addresses data duplication, provenance loss, and governance challenges endemic to disparate or ad hoc analytics environments (Abgrall et al., 10 Jul 2024, Valiaiev, 15 Nov 2025).

1. Formal Foundations and Mathematical Model

At the core of a SOT repository is the Semantic SQL Transducer (SST), formalized as a function $T: D \to V$ that maps any database instance $d \in D$—the set of all raw relational table instances—to a conceptualized, semantically enriched “truth” view $v = T(d) \in V$. This mapping ensures that every consumer downstream uses a shared, canonical representation of the data (Abgrall et al., 10 Jul 2024).

The SST possesses the following properties:

• Losslessness: There exists an inverse operator $T^{-1}$ (not necessarily SQL-computable) such that $T^{-1}(T(d)) \equiv d$ for all $d \in D$, ensuring that no source data is discarded.
• Consistency (Monotonicity): For $d_1 \subseteq d_2$, $T(d_1) \subseteq T(d_2)$, supporting incremental refresh and extension.
• Uniqueness: If $T(d_1) = T(d_2)$, then $d_1 \equiv d_2$, preserving provenance and prohibiting unintended collapse of source differences.

The algebraic specification uses standard relational-algebra operators (projection $\pi$, renaming $\rho$, selection $\sigma$) to synthesize surrogate identifiers (e.g., POID, DOID for Person and Department) and fully normalize the final SOT view. Intermediate relations are defined and assembled through join-trees on the generated keys, explicitly filtering out incomplete rows (Abgrall et al., 10 Jul 2024).

2. Architecture, Data Pipeline, and Materialization

The canonical pipeline for constructing and maintaining a SOT SQL repository is organized into six sequential stages:

1. Source Ingestion: Raw tables—such as PERSON_RAW and DEPT_RAW—are loaded into staging schemas and subjected to minimal cleaning (type casts, deduplication).
2. Semantic Modeling: Business metadata (glossary, ontology) assigns meaning to attributes and formalizes the mapping to surrogate IDs; e.g., $POID = f_1(SSN)$ and $DOID = f_2(Dept)$ via sequences or hash-maps.
3. Transducer Construction: Emit the SST’s sequence of $\rho/\pi/\sigma$ relational-algebra steps, enforcing null-elimination and domain-specific predicates.
4. Join and Assembly: Create fully linked canonical views through systematic joins on surrogate keys—ensuring each entity and relationship is represented without ambiguity.
5. Materialization: Output the result as a MATERIALIZED VIEW or persistent table (e.g., SOT_PERSON_DEPT), leveraging incremental update via change data capture (CDC) or database triggers, in accord with the monotonicity property.
6. Distribution and Lineage: Expose the SOT view to downstream analytics tools and data marts, with all lineage and transformation rules traceable via metadata catalogs integrated into enterprise governance frameworks (Abgrall et al., 10 Jul 2024).

All mapping rules and entity definitions are versioned in a metadata catalog, supporting automated SQL generation and resilience to source schema evolution.

3. CI/CD Governance and DataOps Controls

The operational integrity, transparency, and auditability of the SOT repository are enforced through a DataOps-aligned CI/CD validation framework (Valiaiev, 15 Nov 2025). This framework is sequenced into five pipeline stages—Lint, Optimize, Parse, Validate, and Observe—each comprising automated, testable checks.

Pipeline Stage Overview

Stage	Purpose	Example Checks
Lint	Code style, naming, tagging, syntactic uniformity	check_naming_convention, check_sql_lint, check_tags
Optimize	Logic duplication, AI-based review	check_vector_similarity, check_ai_feedback
Parse	Structural validation, dead code, CTE hierarchy	check_ast_parse, check_column_usage
Validate	Governance, access, compliance	check_owner, check_freeze_schedule, check_model_dependencies
Observe	Testing, runtime, observability	check_configured_test, check_runtime_threshold

Pipeline failures block deployment, strictly aligning the repository with governance and SOT principles.

A DataOps Controls Scorecard enumerates twelve governance controls, including Versioning, Consistency, Documentation, Ownership, Testing, Validation, Uniqueness, Performance, Automation, Observability, Delivery, and Rollback. Table mapping in the framework links each control to explicit CI/CD checks, with status verified or supported (Valiaiev, 15 Nov 2025).

4. Implementation Techniques and Best Practices

A SOT SQL repository is structured into three distinct layers:

1. Staging: Raw, uncurated data tables—read-only, subject to minimal transformation.
2. Semantic Models: Metadata-driven SQL generators and semantic mapping logic, version-controlled and parameterized from governance glossaries.
3. SOT Layer: Authoritative, never hand-edited materialized views/tables. All analytics must consume from this layer (Abgrall et al., 10 Jul 2024).

Lineage and audit features are embedded via explicit comments, lineage tables, and integration with catalog tools (e.g., Apache Atlas, Collibra). Schema versioning and change propagation use version stamps and automated view regeneration. Performance is supported by indexing surrogate-key mappings, pushdown filtering, strategic partitioning, and view caching. Operational resilience is assured by containerized CI stages, parallelized job execution, and checkpointed rollbacks (Abgrall et al., 10 Jul 2024, Valiaiev, 15 Nov 2025).

5. SOT Principle Enforcement and Business-Technical Bridging

The SOT principle mandates that downstream analytics are strictly prohibited from consuming non-canonical or ad hoc sources. All transformations are governed by generated SQL from centrally managed metadata—ensuring changes in business semantics or technical definitions propagate consistently throughout the pipeline.

To bridge the business-technical divide, entity-relationship definitions and attribute glossaries are maintained in a central governance store. The SQL transducer generator natively reads these—via JSON, YAML, or glossaries—and outputs both machine-executable SQL and human-readable documentation, ensuring synchronization between theoretical models, operational logic, and business nomenclature (Abgrall et al., 10 Jul 2024).

6. Impact, Challenges, and Significance

SOT SQL repositories, formalized via the SST and governed by DataOps CI/CD, furnish organizations with the “one true version” for every domain concept, fundamentally mitigating risk associated with logic replication, schema drift, and untracked analytic changes (Abgrall et al., 10 Jul 2024, Valiaiev, 15 Nov 2025). Full audit trails, rollback capability, and lineage make regulatory compliance and provenance validation tractable. By embedding semantic modeling and strict CI/CD governance, the system addresses historic weaknesses of siloed SQL data engineering, enabling scalable, transparent, and collaborative analytics development.

A plausible implication is that such rigor limits operational flexibility in favor of reproducibility and control; tuning the balance between agility and governance is nontrivial, especially in organizations with heterogeneous legacy processes.

• “Understanding the Semantic SQL Transducer” (Abgrall et al., 10 Jul 2024)
• “DataOps-driven CI/CD for analytics repositories” (Valiaiev, 15 Nov 2025)