Design of BI Environments and Information Architectures
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Diseñamos ecosistemas de Business Intelligence robustos, gobernados y preparados para la analítica avanzada. Desde la capa de ingesta hasta la capa semántica, creamos arquitecturas que garantizan escalabilidad, rendimiento y confianza en el dato.
Problemas habituales
- • Proliferación de silos y pipelines ad‑hoc.
- • Altos costes por infra‑overprovisioning.
- • Falta de linaje claro entre origen y KPI.
- • Rendimiento desigual con picos de concurrencia.
Nuestro enfoque
Aplicamos principios de Data Mesh y Lakehouse para separar responsabilidades, estandarizar contratos de datos y habilitar dominios autosuficientes.
Combinamos Design Thinking con marcos de referencia (Kimball, Inmon, Data Vault) para adaptar la arquitectura al nivel de madurez y presupuesto.
Arquitectura de referencia
| Capa | Función | Tecnologías recomendadas |
|---|---|---|
| Ingesta | Captura de cambios Batch & streaming | ADF | Glue | Kafka |
| Lake Raw | Almacenamiento parquet/Delta | S3 | ADLS Gen2 | GCS |
| Lake Curated | Modelo de dominio Data Vault | Databricks | Snowflake |
| Warehouse | SQL analítico Agregaciones | Synapse | Redshift | BigQuery |
| Modelado semántico | KPIs reutilizables | Power BI | Looker |
| Consumo | Dashboards & API | Tableau | REST/GraphQL |
Fases del proyecto
1. Diagnóstico & roadmap
Assessment de madurez, inventario de fuentes y arquitectura propuesta.
2. MVP Lakehouse
Despliegue de un dominio crítico con pipelines e informes base.
3. Gobernanza & seguridad
Catálogo, linaje y RLS/CLS en el modelo.
4. Escalado de dominios
Onboarding de nuevas áreas y factoría de datos.
5. Optimización continua
Monitoreo de coste, rendimiento y adopción.
Herramientas clave
dbt & Data Build Tool
Apache Airflow
Databricks Delta
Great Expectations
Resultados
95 %
Reducción del tiempo de onboarding de fuente
+4 pp
Aumento de confianza en el dato (encuesta interna)
99,9 %
Disponibilidad del entorno
6 sem
Payback típico
Buenas prácticas imprescindibles
- • Separar compute y storage para optimizar costes.
- • Implementar blue/green deploy en el warehouse.
- • Medir data freshness y data downtime con SLAs.
- • Adoptar nomenclatura común (CONFORMED).
- • Automatizar documentación con herramientas de catálogo.
We design robust, governed Business Intelligence ecosystems prepared for advanced analytics. From ingestion layer to semantic layer, we create architectures that guarantee scalability, performance, and data trust.
Common problems
- • Proliferation of silos and ad-hoc pipelines.
- • High costs due to infra overprovisioning.
- • Lack of clear lineage between source and KPI.
- • Uneven performance with concurrency spikes.
Our approach
We apply Data Mesh and Lakehouse principles to separate responsibilities, standardize data contracts and enable self-sufficient domains.
We combine Design Thinking with reference frameworks (Kimball, Inmon, Data Vault) to adapt architecture to maturity and budget levels.
Reference architecture
| Layer | Function | Recommended technologies |
|---|---|---|
| Ingestion | Change capture Batch & streaming | ADF | Glue | Kafka |
| Lake Raw | Storage parquet/Delta | S3 | ADLS Gen2 | GCS |
| Lake Curated | Domain model Data Vault | Databricks | Snowflake |
| Warehouse | Analytical SQL Aggregations | Synapse | Redshift | BigQuery |
| Semantic model | Reusable KPIs | Power BI | Looker |
| Consumption | Dashboards & API | Tableau | REST/GraphQL |
Project phases
1. Diagnosis & roadmap
Maturity assessment, source inventory and proposed architecture.
2. MVP Lakehouse
Deployment of a critical domain with base pipelines and reports.
3. Governance & security
Catalog, lineage and RLS/CLS in the model.
4. Domain scaling
Onboarding new areas and data factory.
5. Continuous optimization
Monitoring cost, performance and adoption.
Key tools
dbt & Data Build Tool
Apache Airflow
Databricks Delta
Great Expectations
Results
95 %
Reduced onboarding time
+4 pp
Increased data trust (internal survey)
99.9 %
Environment availability
6 weeks
Typical payback
Essential best practices
- • Separate compute and storage to optimize costs.
- • Implement blue/green deploy in the warehouse.
- • Measure data freshness and data downtime with SLAs.
- • Adopt common nomenclature (CONFORMED).
- • Automate documentation with catalog tools.
Diseñamos ecosistemas de Business Intelligence robustos, gobernados y preparados para la analítica avanzada. Desde la capa de ingesta hasta la capa semántica, creamos arquitecturas que garantizan escalabilidad, rendimiento y confianza en el dato.
Problemas habituales
- • Proliferación de silos y pipelines ad‑hoc.
- • Altos costes por infra‑overprovisioning.
- • Falta de linaje claro entre origen y KPI.
- • Rendimiento desigual con picos de concurrencia.
Nuestro enfoque
Aplicamos principios de Data Mesh y Lakehouse para separar responsabilidades, estandarizar contratos de datos y habilitar dominios autosuficientes.
Combinamos Design Thinking con marcos de referencia (Kimball, Inmon, Data Vault) para adaptar la arquitectura al nivel de madurez y presupuesto.
Arquitectura de referencia
| Capa | Función | Tecnologías recomendadas |
|---|---|---|
| Ingesta | Captura de cambios Batch & streaming | ADF | Glue | Kafka |
| Lake Raw | Almacenamiento parquet/Delta | S3 | ADLS Gen2 | GCS |
| Lake Curated | Modelo de dominio Data Vault | Databricks | Snowflake |
| Warehouse | SQL analítico Agregaciones | Synapse | Redshift | BigQuery |
| Modelado semántico | KPIs reutilizables | Power BI | Looker |
| Consumo | Dashboards & API | Tableau | REST/GraphQL |
Fases del proyecto
1. Diagnóstico & roadmap
Assessment de madurez, inventario de fuentes y arquitectura propuesta.
2. MVP Lakehouse
Despliegue de un dominio crítico con pipelines e informes base.
3. Gobernanza & seguridad
Catálogo, linaje y RLS/CLS en el modelo.
4. Escalado de dominios
Onboarding de nuevas áreas y factoría de datos.
5. Optimización continua
Monitoreo de coste, rendimiento y adopción.
Herramientas clave
dbt & Data Build Tool
Apache Airflow
Databricks Delta
Great Expectations
Resultados
95 %
Reducción del tiempo de onboarding de fuente
+4 pp
Aumento de confianza en el dato (encuesta interna)
99,9 %
Disponibilidad del entorno
6 sem
Payback típico
Buenas prácticas imprescindibles
- • Separar compute y storage para optimizar costes.
- • Implementar blue/green deploy en el warehouse.
- • Medir data freshness y data downtime con SLAs.
- • Adoptar nomenclatura común (CONFORMED).
- • Automatizar documentación con herramientas de catálogo.
We design robust, governed Business Intelligence ecosystems prepared for advanced analytics. From ingestion layer to semantic layer, we create architectures that guarantee scalability, performance, and data trust.
Common problems
- • Proliferation of silos and ad-hoc pipelines.
- • High costs due to infra overprovisioning.
- • Lack of clear lineage between source and KPI.
- • Uneven performance with concurrency spikes.
Our approach
We apply Data Mesh and Lakehouse principles to separate responsibilities, standardize data contracts and enable self-sufficient domains.
We combine Design Thinking with reference frameworks (Kimball, Inmon, Data Vault) to adapt architecture to maturity and budget levels.
Reference architecture
| Layer | Function | Recommended technologies |
|---|---|---|
| Ingestion | Change capture Batch & streaming | ADF | Glue | Kafka |
| Lake Raw | Storage parquet/Delta | S3 | ADLS Gen2 | GCS |
| Lake Curated | Domain model Data Vault | Databricks | Snowflake |
| Warehouse | Analytical SQL Aggregations | Synapse | Redshift | BigQuery |
| Semantic model | Reusable KPIs | Power BI | Looker |
| Consumption | Dashboards & API | Tableau | REST/GraphQL |
Project phases
1. Diagnosis & roadmap
Maturity assessment, source inventory and proposed architecture.
2. MVP Lakehouse
Deployment of a critical domain with base pipelines and reports.
3. Governance & security
Catalog, lineage and RLS/CLS in the model.
4. Domain scaling
Onboarding new areas and data factory.
5. Continuous optimization
Monitoring cost, performance and adoption.
Key tools
dbt & Data Build Tool
Apache Airflow
Databricks Delta
Great Expectations
Results
95 %
Reduced onboarding time
+4 pp
Increased data trust (internal survey)
99.9 %
Environment availability
6 weeks
Typical payback
Essential best practices
- • Separate compute and storage to optimize costs.
- • Implement blue/green deploy in the warehouse.
- • Measure data freshness and data downtime with SLAs.
- • Adopt common nomenclature (CONFORMED).
- • Automate documentation with catalog tools.
At Itrion, we provide direct, professional communication aligned with the objectives of each organisation. We diligently address all requests for information, evaluation, or collaboration that we receive, analysing each case with the seriousness it deserves.
If you wish to present us with a project, evaluate a potential solution, or simply gain a qualified insight into a technological or business challenge, we will be delighted to assist you. Your enquiry will be handled with the utmost care by our team.
At Itrion, we provide direct, professional communication aligned with the objectives of each organisation. We diligently address all requests for information, evaluation, or collaboration that we receive, analysing each case with the seriousness it deserves.
If you wish to present us with a project, evaluate a potential solution, or simply gain a qualified insight into a technological or business challenge, we will be delighted to assist you. Your enquiry will be handled with the utmost care by our team.