DataViz Migration - an interoperable semantic layer - {oa-lake-ctx}

Thu, 02 Jul 2026 00:00:00 +0000

Legacy decision-support platforms typically relied on a semantic layer that let users work with business objects rather than tables or SQL queries.

“Customers”, “contracts”, “products”, or “revenue” were exposed in a functional form, independent of the underlying physical storage structures.

To address this need, we designed {oa-lake-ctx}.

{oa-lake-ctx} is the semantic layer of the {oa-lake} architecture. It centralizes business objects, dimensions, measures, KPIs, calculation rules, and security mechanisms.

Key point: consuming tools work with business objects. The physical structures stay hidden.

Architecture

{oa-lake-ctx} centralizes dimensions, measures, hierarchies, KPIs, and calculation rules.

These components are defined once and then exposed to consuming tools.

Unlike some semantic layers on the market, {oa-lake-ctx} requires no specific physical model, no pivot table, and no imposed navigation path.

Relationships are calculated dynamically from the existing model.

Key point: a single business model for all data and all consuming tools.

Data sources

{oa-lake-ctx} can use several data systems simultaneously.

Data can come from Oracle, SQL Server, PostgreSQL, DB2, Snowflake, BigQuery, Redshift databases, files, or other SQL-compatible sources.

Business objects can combine several sources within a single functional definition.

Typical use case: build a KPI from data spread across several platforms.

Business objects

The catalogs exposed by {oa-lake-ctx} group together dimensions, measures, KPIs, hierarchies, and business nomenclature.

Relationships between objects are defined in the semantic layer, not in the reporting tools.

Catalogs can be organized independently of the databases’ physical structures.

SQL generation

SQL queries are generated dynamically from the selected business objects, dimensions, measures, and filters.

Joins, aggregations, and calculation rules are applied by the engine.

User filters are propagated all the way to the source systems via the SQL Push-Down mechanism: no systematic copying of data — queries run against the existing systems, respecting the architectures already in place.

The generated SQL can be executed by {oa-lake} or by any compatible SQL engine.

Key point: users work with business objects; the SQL is generated automatically.

Smart Cache

Data stays in the source systems whenever possible.

The most heavily used datasets can be materialized in Parquet format.

The cache is driven by {oa-lake}’s Cache Manager, managed automatically based on observed usage.

Cache management remains independent of consuming tools.

Key point: the most expensive queries can be accelerated without modifying the source systems.

SQL Engine

{oa-lake} relies on a columnar, in-memory SQL engine.

The queries generated by {oa-lake-ctx} are executed by this engine or propagated to the source systems depending on the context.

The SQL remains portable, standard, and executable on different engines, independent of the storage technologies in use — which limits technology lock-in.

Key point: the SQL engine forms the execution layer of the architecture.

Security

Security is managed at the semantic layer level.

Access restrictions, user profiles, business catalogs, and security filters are applied before queries are generated.

This approach makes it possible to reproduce the security mechanisms found in platforms such as SAP BusinessObjects or IBM Cognos.

Key point: security rules are defined once and then applied to all connected tools.

Reporting

The catalogs exposed by {oa-lake-ctx} can be consumed by any tool capable of querying a PostgreSQL source.

Power BI, Looker, Spotfire, Tableau, Qlik, or other platforms access the same business objects, dimensions, measures, and KPIs.

The functional definitions remain identical regardless of the tool being used.

Result: several platforms can share the same business catalog with no duplication of calculation rules.

Governance

Business objects, KPIs, dimensions, measures, and calculation rules are centralized in a single repository.

Functional changes are made in {oa-lake-ctx} and are then immediately available to all connected tools.

Key point: {oa-lake-ctx} is the central point for defining business objects and KPIs.

DataViz Migration - migrating from Cognos to Power BI

Thu, 02 Jul 2026 00:00:00 +0000

IBM Cognos Analytics platforms typically hold several years’ worth of business modeling: Frameworks, hierarchies, calculations, dimensions, measures, variables, and reports.

{openAudit} analyzes Cognos Frameworks, reports, and usage logs to reconstruct the dependencies between data sources, business objects, and reporting outputs.

Key point: in most Cognos estates, the value lies more in the Frameworks and business rules than in the reports themselves.

Analysis of the Cognos estate

The analysis covers Framework Manager, reports, queries, dimensions, hierarchies, calculations, filters, and variables.

The collected metadata makes it possible to reconstruct:

dependencies between objects;
relationships with data sources;
business objects and indicators exposed in reports.

Scope rationalization

Usage logs are cross-referenced with the platform’s metadata.

This phase identifies unused reports, duplicates, and obsolete Frameworks.

Result: the scope that’s actually used is isolated before migration.

Conversion to Power BI

Cognos Frameworks are analyzed and then converted to the target Power BI model.

Dimensions, hierarchies, measures, calculations, variables, and relationships between objects are reconstructed in the target semantic model.

Cognos expressions are converted to SQL or DAX depending on their nature.

Dependencies between Frameworks, reports, and data sources are preserved in the generated model.

Key point: business objects and calculation rules are reconstructed directly from the Cognos Frameworks.

Validation

Migrations can be carried out with dual Cognos / Power BI execution.

The indicators produced by both platforms are compared during acceptance testing phases.

Typical use case: progressive validation of reports before cutover.

Usage-driven migration

Reports can be migrated in batches based on how often they’re used and their business criticality.

Observed usage forms the basis for project planning and development prioritization.

Result: a reduced volume to migrate, with effort focused on active usage.

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