Simor Consulting
Category: Data Engineering
The case for streaming is straightforward: data that arrives in minutes instead of hours enables decisions that were previously impossible. Fraud detection catches transactions before they clear. Pers
Data Council 2026 wrapped in Austin last week, and the signal-to-noise ratio was higher than in recent years. The conference has historically been the venue where data infrastructure practitioners — n
Most data quality initiatives fail not because teams lack tools, but because they measure the wrong things. Teams track hundreds of data quality metrics, generate dashboards full of green indicators,
Data pipelines built for business intelligence often fail when supporting AI workloads. The root cause is usually architectural: BI pipelines assume bounded, relatively static datasets, while AI syste
Time-travel queries—the ability to access data as it existed at any point in the past—have become essential in modern data platforms. This capability transforms how organizations approach data governa
# Building Synthetic Data Pipelines for ML Testing Synthetic data addresses real ML development problems: privacy restrictions on real data, class imbalance, and edge case coverage. It does not repla
Time series forecasting requires specialized pipeline architecture. Unlike standard batch processing, time series work demands strict chronological ordering, historical context, time-based feature eng
Data contracts are formal agreements that define the structure, semantics, quality standards, and delivery expectations for data exchanged between teams. They specify schema definitions, SLAs, ownersh
Organizations scaling ML efforts encounter a predictable problem: feature engineering work duplicates across teams, training-serving skew causes model failures in production, and point-in-time correct