Dataviz |

DataViz Migration - an upstream/downstream SQL interoperability layer - {oa-lake}

Fri, 03 Jul 2026 00:00:00 +0000

Decision-support platforms often rely on several databases, several data warehouses / data lakes, and several reporting tools.

{oa-lake} introduces an intermediate SQL layer between data sources and dataviz tools.

Business rules, business objects, dimensions, measures, and calculations are centralized in this layer and then exposed to consuming tools.

Key point: data sources and reporting tools become independent of one another.

Architecture

{oa-lake} connects simultaneously to multiple data systems: relational databases, data warehouses, lakehouses, files, or APIs.

Queries are federated by a distributed SQL engine that performs the necessary joins, calculations, and transformations.

The resulting SQL remains standard and executable across different engines, which limits technology lock-in.

Business objects are defined once and then exposed to consuming tools.

Data sources

The data stays in its original systems.

Oracle, SQL Server, PostgreSQL, DB2, BigQuery, Snowflake, Redshift, Azure SQL (and others), or files can be queried within a single request.

The SQL Push-Down mechanism avoids any systematic copying of data: queries run directly against the existing systems, respecting the architectures already in place.

Typical use case: build a dashboard from data spread across several platforms with no prior migration.

SQL business layer

Dimensions, measures, hierarchies, and calculation rules are centralized in {oa-lake}.

This layer becomes the single entry point for reporting tools.

Business rules are no longer duplicated across every dataviz platform.

Key point: a single definition of indicators for the entire decision-support system.

Parquet cache

Data can be materialized in Parquet format.

This caching layer reduces response times and limits load on source systems. It’s managed automatically based on observed usage, with no manual intervention.

Typical use case: speed up the most expensive analytical processing.

Data visualization

Reporting tools connect to the model exposed by {oa-lake}.

Power BI, Looker, Spotfire, Tableau, Qlik, SAP BusinessObjects, or any SQL-compatible tool can use the same business objects.

Switching dataviz tools no longer requires rebuilding dimensions, measures, and calculation rules.

Progressive migration

Several tools can coexist during a transition phase.

The same business model can be exposed simultaneously to several reporting platforms.

Result: progressive migration of usage with no change to the data systems.

Data Lineage

The data lineage reconstructed by {openAudit} remains available across the whole chain.

Dependencies are documented from the source systems all the way to the indicators exposed in the dashboards.

Key point: transformations, calculations, and usage remain traceable across the entire architecture.

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 - SAP BO, Power BI, Spotfire, or Cognos to an open, interoperable architecture

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

Data visualization platforms evolve regularly.

SAP BusinessObjects, IBM Cognos, Power BI, or Spotfire may succeed one another within the same Information System. Business objects, calculations, indicators, and business rules generally remain identical from one platform to the next.

{openAudit} analyzes technical repositories, semantic layers, and reports to reconstruct the dependencies between data sources, intermediate processes, and the objects exposed in dashboards.

Key point: the functional components of reporting platforms are extracted and then rebuilt in a format independent of the target technology.

Analysis of the decision-support estate

The parser-based analysis mechanisms cover SAP BusinessObjects universes, Cognos Frameworks, Power BI models, Spotfire Information Links, as well as the associated reports, dashboards, calculations, filters, and business objects.

The collected metadata makes it possible to reconstruct technical dependencies, semantic layers, and business rules.

Rationalization

Technical repositories are cross-referenced with usage logs. Unused reports, duplicates, and obsolete components are identified before the migration phase.

Result: a reduced scope for the actual migration.

SQL transcription

Queries, calculations, filters, aggregations, joins, variables, and business rules are reconstructed in the form of SQL.

The resulting SQL forms an open representation of the functional logic originally carried by the data visualization platform.

Key point: SQL becomes the pivot format for representing business objects.

{oa-lake}

The SQL processes are integrated into {oa-lake}.

{oa-lake} centralizes business objects, indicators, dimensions, business rules, and SQL transformations. The engine federates several data systems and exposes a single model to reporting tools.

Data can be materialized in Parquet format. Queries are generated against the physical systems or executed from the cache when needed.

Key point: business logic is defined once and then exposed to several data visualization platforms.