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Conectamos, depuramos y orquestamos datos a escala empresarial. Nuestro enfoque ELT + DataOps garantiza pipelines fiables, trazables y preparados para analítica avanzada y machine learning.
Retos habituales
- • Procesos nocturnos que fallan sin aviso.
- • Código ETL monolítico y difícil de mantener.
- • Falta de trazabilidad y calidad del dato.
- • Tiempos de carga que crecen al ritmo del volumen.
Enfoque Itrion
Automatizamos pipelines declarativos con orquestadores modernos (Airflow, Azure Data Factory, dbt). Implementamos pruebas de datos, alertas proactivas y despliegue continuo.
Resultado: tiempo de ciclo menor, errores detectados en minutos y flexibilidad para nuevas fuentes.
Pipeline estándar en 6 fases
Ingesta
CDC, API, streaming
Staging
Raw en formatos Parquet/Delta
Cleansing
Validaciones y estandarización
Transformación
dbt SQL + tests
Carga
Warehouse/Lakehouse
Publ. & linaje
Catalog & métricas de dato
Stack tecnológico
| Capa | Azure | AWS | Open‑Source |
|---|---|---|---|
| Orquestación | Data Factory | Glue Workflows | Apache Airflow |
| Transformación | Synapse SQL | Redshift Spectrum | dbt Core |
| Streaming | Event Hubs | Kinesis | Kafka |
| Calidad de dato | Purview | Deequ | Great Expectations |
Impacto medible
‑70 %
Errores de carga
4×
Velocidad de procesamiento
99,9 %
Disponibilidad
8 sem
ROI medio
Buenas prácticas críticas
- • Versiona pipelines y tests en Git; despliega con CI.
- • Diseña para fallar: retry exponencial y idempotencia.
- • Separa capa raw, clean y business.
- • Agrega pruebas de calidad en cada paso (expectations).
- • Registra linaje automático y métricas de frescura.
We connect, cleanse, and orchestrate data at enterprise scale. Our ELT + DataOps approach guarantees reliable, traceable pipelines prepared for advanced analytics and machine learning.
Common challenges
- • Night processes failing silently.
- • Monolithic ETL code, hard to maintain.
- • Lack of traceability and data quality.
- • Load times growing with volume.
Itrion approach
We automate declarative pipelines with modern orchestrators (Airflow, Azure Data Factory, dbt). We implement data tests, proactive alerts, and continuous deployment.
Result: shorter cycle times, errors caught in minutes, and flexibility for new sources.
Standard pipeline in 6 stages
Ingestion
CDC, API, streaming
Staging
Raw in Parquet/Delta formats
Cleansing
Validations and standardization
Transformation
dbt SQL + tests
Loading
Warehouse/Lakehouse
Publishing & lineage
Catalog & data metrics
Technology stack
| Layer | Azure | AWS | Open‑Source |
|---|---|---|---|
| Orchestration | Data Factory | Glue Workflows | Apache Airflow |
| Transformation | Synapse SQL | Redshift Spectrum | dbt Core |
| Streaming | Event Hubs | Kinesis | Kafka |
| Data quality | Purview | Deequ | Great Expectations |
Measurable impact
‑70 %
Load errors
4×
Processing speed
99.9 %
Availability
8 weeks
Average ROI
Critical best practices
- • Version pipelines and tests in Git; deploy with CI.
- • Design to fail: exponential retry and idempotency.
- • Separate raw, clean, and business layers.
- • Add quality checks at every step (expectations).
- • Register automatic lineage and freshness metrics.
Conectamos, depuramos y orquestamos datos a escala empresarial. Nuestro enfoque ELT + DataOps garantiza pipelines fiables, trazables y preparados para analítica avanzada y machine learning.
Retos habituales
- • Procesos nocturnos que fallan sin aviso.
- • Código ETL monolítico y difícil de mantener.
- • Falta de trazabilidad y calidad del dato.
- • Tiempos de carga que crecen al ritmo del volumen.
Enfoque Itrion
Automatizamos pipelines declarativos con orquestadores modernos (Airflow, Azure Data Factory, dbt). Implementamos pruebas de datos, alertas proactivas y despliegue continuo.
Resultado: tiempo de ciclo menor, errores detectados en minutos y flexibilidad para nuevas fuentes.
Pipeline estándar en 6 fases
Ingesta
CDC, API, streaming
Staging
Raw en formatos Parquet/Delta
Cleansing
Validaciones y estandarización
Transformación
dbt SQL + tests
Carga
Warehouse/Lakehouse
Publ. & linaje
Catalog & métricas de dato
Stack tecnológico
| Capa | Azure | AWS | Open‑Source |
|---|---|---|---|
| Orquestación | Data Factory | Glue Workflows | Apache Airflow |
| Transformación | Synapse SQL | Redshift Spectrum | dbt Core |
| Streaming | Event Hubs | Kinesis | Kafka |
| Calidad de dato | Purview | Deequ | Great Expectations |
Impacto medible
‑70 %
Errores de carga
4×
Velocidad de procesamiento
99,9 %
Disponibilidad
8 sem
ROI medio
Buenas prácticas críticas
- • Versiona pipelines y tests en Git; despliega con CI.
- • Diseña para fallar: retry exponencial y idempotencia.
- • Separa capa raw, clean y business.
- • Agrega pruebas de calidad en cada paso (expectations).
- • Registra linaje automático y métricas de frescura.
We connect, cleanse, and orchestrate data at enterprise scale. Our ELT + DataOps approach guarantees reliable, traceable pipelines prepared for advanced analytics and machine learning.
Common challenges
- • Night processes failing silently.
- • Monolithic ETL code, hard to maintain.
- • Lack of traceability and data quality.
- • Load times growing with volume.
Itrion approach
We automate declarative pipelines with modern orchestrators (Airflow, Azure Data Factory, dbt). We implement data tests, proactive alerts, and continuous deployment.
Result: shorter cycle times, errors caught in minutes, and flexibility for new sources.
Standard pipeline in 6 stages
Ingestion
CDC, API, streaming
Staging
Raw in Parquet/Delta formats
Cleansing
Validations and standardization
Transformation
dbt SQL + tests
Loading
Warehouse/Lakehouse
Publishing & lineage
Catalog & data metrics
Technology stack
| Layer | Azure | AWS | Open‑Source |
|---|---|---|---|
| Orchestration | Data Factory | Glue Workflows | Apache Airflow |
| Transformation | Synapse SQL | Redshift Spectrum | dbt Core |
| Streaming | Event Hubs | Kinesis | Kafka |
| Data quality | Purview | Deequ | Great Expectations |
Measurable impact
‑70 %
Load errors
4×
Processing speed
99.9 %
Availability
8 weeks
Average ROI
Critical best practices
- • Version pipelines and tests in Git; deploy with CI.
- • Design to fail: exponential retry and idempotency.
- • Separate raw, clean, and business layers.
- • Add quality checks at every step (expectations).
- • Register automatic lineage and freshness metrics.
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.