Data

Databases, pipelines, analytics, ML ops and data quality.

DataJuly 27, 20264 min read

PostgreSQL 18: why uuidv7 and I/O matter to product teams

PostgreSQL 18 brings technical improvements that directly affect performance, migrations and data modeling.

postgresqldatabaseperformance

DataJuly 26, 20264 min read

Iceberg brings snapshots, schema evolution and hidden partitioning to large analytical datasets.

lakehouseiceberganalytics

DataJuly 25, 20264 min read

Adding a vector database does not make an assistant reliable. Documents, chunks and metadata often determine the result.

ragvectorsai

DataJuly 24, 20264 min read

Duplicates, incomplete fields and contradictory definitions become more dangerous when a model reformulates them confidently.

qualityaigovernance

DataJuly 23, 20264 min read

Teams monitor APIs with traces and metrics. Data pipelines deserve the same rigor to understand delays and errors.

observabilitypipelinedata

DataJuly 22, 20264 min read

The semantic layer promises shared metrics across BI, notebooks and AI. It mostly requires clear governance.

analyticssemantic-layerbi

DataJuly 21, 20264 min read

Streaming is powerful, but it adds cost and complexity. The right pace depends on the decision being made.

streamingreal-timearchitecture

DataJuly 20, 20264 min read

Teams want to give models more context. The best protection is sometimes not sending the data.

privacyaigovernance

DataJuly 19, 20264 min read

Synthetic data can protect privacy and speed up tests. It must still be compared with reality.

synthetic-datatestsprivacy

DataJuly 18, 20264 min read

Latency and availability are not enough. In production, a model can stay online while getting worse.

mlopsmonitoringai