Designing Reliable Data Pipelines
What separates a pipeline that survives production from one that quietly breaks under real-world data — orchestration, monitoring, and fault handling patterns that hold up.
Insights
Notes on data engineering and infrastructure.
Practical writing on pipelines, transformation, infrastructure, and governance from the Stratum Data team.
Data Lakes vs. Data Warehouses vs. Lakehouses
A practical comparison of the three dominant storage architectures, and the questions worth asking before committing to one.
Building Secure Data Infrastructure
How access controls, classification, and encryption fit together across the data lifecycle — from ingestion to analytics.
Data Quality and Transformation Best Practices
Validation, deduplication, and enrichment patterns that turn inconsistent raw data into something teams can actually rely on.
Understanding Data Lineage
Why knowing where data came from and how it changed matters as much as the data itself — and how lineage tracking actually works.
Scaling Data Engineering Workloads
Approaches to keeping pipelines and storage performant as data volume and organizational demand both grow.
Batch vs. Streaming Data Pipelines
When near-real-time streaming is worth the added complexity, and when batch processing remains the more reliable choice.
Have a data challenge in mind?
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