Databricks Lakebase: The Reality of Postgres inside the Lakehouse
What Databricks Lakebase really delivers: Federation vs Lakehouse Sync, copy-on-write branching for CI/CD, cold starts, token expiry and governance.
Data Engineering
Data Engineering Partner Evaluation: An RFP Scorecard
A weighted RFP scorecard for data engineering partner evaluation: knockout gates, seven scored criteria, a paid pilot on your data and reference checks.
Paweł Szczepanik
Stop Parsing Data Manually: How AI Extracts Structure from Unstructured Data
How LLMs extract structure from unstructured data on Databricks: sampling, schema-enforced outputs, validation, quarantine tables and human review.
Andrzej Gabryel
How to Choose a Data Platform for AI Workloads
An AI data platform is judged by the workloads it runs: training, inference, vector retrieval, feature serving and model governance. Here is how to choose.
Paweł Szczepanik
AI-Ready Data Foundations: The Real AI Project Blocker
AI-ready data foundations decide which AI projects ship: four readiness dimensions, a use-case-scoped audit and how to price the gaps.
Paweł Szczepanik
Data Platform Modernization for Fintech and Insurance
Data platform modernization for fintech and insurance under DORA: target architecture, migration sequencing and keeping regulatory reporting live.
Paweł Szczepanik
Nearshore Data Engineering Teams in Poland: A Buyer's Guide
Nearshore data engineering teams in Poland: staffing or delivery, three engagement models, the EU jurisdiction case and how to vet a partner.
Paweł Szczepanik
Apache Airflow Managed Service: What It Should Include
What an Apache Airflow managed service includes, how it differs from MWAA and Cloud Composer, and how providers define, measure and accept data outcomes.
Paweł Szczepanik
Databricks Consulting Services: Implementation Guide
What Databricks consulting services cover, from lakehouse design and migration to Unity Catalog governance and DBU costs, plus how to judge a partner.
Paweł Szczepanik
In-House vs Outsourced Data Engineering: 2026 Guide
A decision framework for in-house vs outsourced data engineering, covering total cost, knowledge risk, scalability, and the hybrid model.
Paweł Szczepanik
Build vs Buy Data Platform: A Layered Decision Framework
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.
Paweł Szczepanik
Data Lakehouse vs Data Warehouse: 2026 Guide
Data lakehouse vs data warehouse compared: architecture, cost, governance, ML support, and how to choose the right platform for enterprise workloads in 2026.
Paweł Szczepanik