Topic
7 articles tagged “Data Engineering”.
Not every ML team can move data into a cloud managed service. Kubeflow is seven projects that turn a Kubernetes cluster into a training, tuning and serving platform, and it is not for everyone.
AWS quietly killed Quantum Ledger Database, and its own migration advice points at Postgres. Here's the append-only, audit-grade ledger we actually build there, what you get for free, and the one thing you have to add back by hand.
Deduping messy real-world data, companies, addresses, people, looks like a solved problem until you do it. Here's an honest comparison of the three techniques that actually matter inside Postgres, when each one wins, and where each one quietly breaks.
Most AI projects fail in the data layer, not the model layer. The slide deck is about agents and RAG; the work that decides the outcome is unglamorous data engineering nobody scoped or staffed.
After replacing Mondrian's SQL emitter with Calcite, the next obvious target was the data path itself: an Apache Arrow segment cache and ADBC connections to replace JDBC. The first benchmarks said Arrow was 2.6x slower. They were wrong, but not for the reasons I expected. Here's the honest report on where columnar wins, where it ties, and where it actually loses.
Concept to Cloud partnered with Consilient to engineer a secure cross-bank fraud detection system using federated learning, overcoming strict banking security requirements and data inconsistencies.
Explore how cloud computing transforms collaborative research by enabling seamless data sharing and real-time teamwork across distributed global teams.
