Data platform architecture | Practical guide
DataApr 12, 2026
By Valendra
Complete guide to data platform architecture: contracts, lineage, BigQuery, Airflow, and governance. Everything you need to move from storage to a real platform.
Data Engineering
Your data,in order
Data Engineering
Your data,in order
Pipelines that scale with youTalk to an expert
Pipelines that scale with you
Talk to an expertWhat we do
We build Lakehouse platforms where all teams access the same data, always updated and trustworthy.
Pipelines that process events instantly. Detect fraud, personalize offers, and react in seconds.
If corrupted data arrives, the pipeline stops and alerts. Never again make decisions based on wrong data.
We replace fragile ETL processes with modern orchestration. Maintainable code that doesn't fail silently.
Business explores data without depending on IT. Dashboards and metrics that answer business questions directly.
What we solve
Common data problems and how we address them
Solución
Pipelines with automated tests and observability. Alerts before bad data arrives.
Data architecture that scales
Each layer designed for reliability, speed, and governance
Ingestion
SaaS connectors, change capture, and streaming. Data from any source in real time.
Ingestion
SaaS connectors, change capture, and streaming. Data from any source in real time.
Storage
Unified Data Warehouse and Data Lake. Open formats and compute/storage separation.
Storage
Unified Data Warehouse and Data Lake. Open formats and compute/storage separation.
Transformation
SQL modeling with automated tests. Full lineage and version control.
Transformation
SQL modeling with automated tests. Full lineage and version control.
Consumption
Dashboards, applications, and data APIs. Self-service for the entire organization.
Consumption
Dashboards, applications, and data APIs. Self-service for the entire organization.
Governance
Data catalog, automated quality, and observability. Full lifecycle control.
Governance
Data catalog, automated quality, and observability. Full lifecycle control.
Explore more solutions
Data engineering questions
What data engineering work do you cover?
We build modern data platforms, real-time pipelines, quality controls, ETL modernization, and self-service analytics.
How do you improve trust in dashboards?
We add automated tests, observability, quality alerts, and lineage so teams can understand and rely on the data behind their dashboards.
Can you modernize fragile ETL pipelines?
Yes. We replace fragile processes with modular orchestration, testable code, and documented lineage.
How do you control data platform costs?
We use query optimization, smart partitioning, materialized views, and architectures designed for cost efficiency.
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