PLATFORM CAPABILITY · CONNECT & GOVERN
Data Transformation
Orchestrate Spark and dbt workflows over governed data. Run batch, streaming or hybrid transformations on your existing stores, no new infrastructure, no data copies.
Every new analytical use case adds a pipeline, a new store and another governance gap.
Spark jobs and dbt models running in parallel environments, each landing outputs in a separate store. Quality rules applied inconsistently across pipelines. No shared lineage connecting source data to transformation output. When an auditor asks where a number comes from, data engineers spend days tracing it manually. The same logic rebuilt for every new consumer, in a new environment, disconnected from the governed data foundation.
Batch, streaming & hybrid
processing in one orchestration layer.
Zero
data duplication: transformation runs over the virtualised layer.
One
governed environment for Spark and dbt, aligned to the Semantic Layer.
WHAT IT DELIVERS
Governed transformation. No new infrastructure.
Transformation workflows run directly over the virtualised data layer, inside the governance perimeter. Quality rules apply before data reaches any downstream consumer. Lineage is built in at every step.
Stratio orchestrates Spark jobs and dbt models from a single visual interface. Teams choose the engine that fits the workload. Both operate on the same governed data foundation and produce outputs aligned to the Semantic Layer.
Whether the workload requires daily batch runs, real-time stream processing or a hybrid of both, Stratio handles it from one orchestration layer, without managing separate environments.
Transformation results are exposed through the governed layer as data products. There is no requirement to land transformed data in a new warehouse or lake to make it available downstream.
01
Transformation logic is defined using the visual workflow editor, or with AI-assisted onboarding of existing dbt models and Spark jobs into the governed environment. Quality rules are attached at design time, within the same governance context.
02
Workflows are scheduled or triggered and run against the virtualised data layer. Batch and streaming jobs are orchestrated from the same surface. Execution is logged with full lineage at every step.
03
Transformation outputs are registered as governed data products, available to downstream consumers, analytics, AI, APIs, through the Data Marketplace and Agentic AI layer.
Automated regulatory lifecycle with explainable AI, lineage and built-in data quality.
-
60→14
Person-days per single regulatory report
-
−80%
Effort per regulatory report
-
<3 mo
Time to first measurable results
-
−20%/ +20%
Tech cost vs. transactional volume (5y)
We work with data engineering and analytics teams across regulated industries in Europe, UK and LATAM. Book a session to see how transformation integrates with the governed platform.
