Most AI projects stall before production. Why data engineering teams should run a business-first Proof-of-Value instead of a Proof-of-Concept to show ROI.
Andrzej Gabryel
Declarative Delta Live Tables or imperative Databricks Workflows? How to choose an orchestration pattern that fits your data platform and your team.
An AI-powered operating model for data engineering turns noisy pipeline failures into fast, auditable recoveries, with earlier detection and safe fixes.
Andrzej Gabryel
Automating Databricks deployments with DAB templates and CI/CD tools ensures fast, error-free pipelines - read to learn how.
Unity Catalog and Volumes: a data engineer's guide to modern data governance in Databricks, covering access control, lineage, and managed storage.
Scalable data pipelines by design: learn batch, streaming, and layered architectures, with patterns and trade-offs for building reliable data systems.
Master Databricks workflow orchestration – automate tasks, integrate Airflow & ADF, and optimize pipelines. Boost efficiency with dynamic scheduling!
Configure the Celery Kubernetes Executor in Airflow 2.0 to combine Celery's scalability with Kubernetes' resource optimization for efficient workflows.
A relational database stores data in tables linked by keys and queried with SQL. Learn how it works, ACID, popular systems, and when to use one.
Michal Slupski
Data engineering trends for 2025: AI-driven automation, Lakehouse architecture, serverless and edge computing, plus more sustainable data practices.
Data warehouse vs data lake vs lakehouse: how the three architectures differ in structure, limitations and use cases, and when each one is the right fit.
Under the CAP theorem, a distributed system can only guarantee two of consistency, availability and partition tolerance. Trade-offs and cloud examples.
Grzegorz Wyczolkowski