Latest blog posts by Paweł Szczepanik

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.

A practical guide to what Databricks consulting services cover, the phases of a real implementation, and how a CTO or Head of Data can judge a partner before signing.

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.

Context engineering decides what your AI agents see at the moment they act. Here is what it is, why it emerged, and how to build the skill in your team.

A decision framework for RAG vs fine-tuning: five criteria, a total cost view, and when to combine both, built for CTOs and Heads of AI in 2026.

MLOps vs LLMOps explained: how operating LLMs differs from classic ML—evaluation, prompts, cost, monitoring—and what your team needs to run GenAI in production.

Data lakehouse vs data warehouse compared: architecture, cost, governance, ML support, and how to choose the right platform for enterprise workloads in 2026.

How agentic AI is reshaping data engineering—autonomous pipeline agents, real use cases, risks, and what enterprise teams should adopt in 2026.

A clear guide to data pipeline architecture: core layers, ETL vs ELT, batch vs streaming, and the patterns enterprise data teams use in 2026.

Enterprise RAG architecture explained: the layers, design trade-offs, and production concerns—retrieval, reranking, guardrails, cost, and grounding.