Data engineering
Data platforms that can be rerun, checked, and explained
SEYON builds pipelines, models, and quality controls for operational and clinical data, from ingestion through analytics.
Workflow
Data engineering workflow
The same path applies whether the first source is a database, a file drop, or an API.
- 1Source systems
- 2Ingestion
- 3Transformation
- 4Data quality
- 5Data warehouse
- 6Analytics
Running step 1: Source systems
Platform
A running view of the data path
Sources land in a raw zone, checks promote them, and curated tables are what analytics and agents are allowed to read.
Running example
From source systems to a curated model
Sources
- EHR
- Labs
- Claims
- Devices
Data platform
Raw
Files and extracts as they arrived
Clean
Checked keys, types, and volumes
Curated
The model analytics can read
Consumption
- Care operations
- Analytics
- AI agent tools
Reading EHR
Services
What a data engineering engagement can include
Integration and governance sit beside the pipeline. They are how new sources join the platform without a one-off script.
- Data pipeline development
- ETL and ELT
- Data warehouse design
- Data lake architecture
- SQL development and optimization
- Data quality validation
- Data transformation
- Data integration
- Reporting and analytics
- Batch and near-real-time processing
- Data governance
Capabilities
Featured capabilities
Open a topic for the engineering approach behind it.
Technology examples
Platforms and languages often used
Apache Spark, cloud data services, and Power BI are examples of tools that fit this work when the workload calls for them. Listing a tool is not a certification or a claim that every engagement uses it.
- Python
- SQL
- PostgreSQL
- SQL Server
- Apache Spark
- Azure Data Factory
- AWS data services
- Google Cloud data services
- Power BI
Discuss your data project
Describe the sources, the reports that depend on them, and where the current process fails.