Latest blog posts by Paweł Szczepanik

A delivery team's view of GenAI proof of concept development: success criteria for production, six failure points after the pilot, and a 90-day roadmap.

Data governance for AI extends classic governance to training data and RAG. What the EU AI Act requires and how to run catalog, policy and audit.

A real-time data pipeline holds up in production when it handles state, out-of-order events, load spikes and failures without losing records.

Batch vs streaming data processing comes down to how fresh your data must be. A framework based on freshness, volume and cost, plus micro-batch and Kappa.

Models degrade quietly when inputs go bad. Data quality dimensions that matter for machine learning, tests with contracts and dbt, and drift monitoring.

What generative AI consulting services cover, from use-case strategy and RAG to governance and LLMOps, and why they move pilots into production.

A decision framework for in-house vs outsourced data engineering, covering total cost, knowledge risk, scalability, and the hybrid model.

What Snowflake migration services involve in 2026, phase by phase, plus the warehouse-sizing and credit pitfalls that inflate cost and how to choose a partner.

What Databricks consulting services cover, from lakehouse design and migration to Unity Catalog governance and DBU costs, plus how to judge a partner.

Build vs buy is the wrong question for a data platform. A layer-by-layer framework, honest TCO, and a third option: buy plus an implementation partner.