Google opens its lakehouse to AWS and Azure data for AI agents
BigQuery, Apache Iceberg and Knowledge Catalog aim to unify multicloud data without moving everything: the promise, architecture and limits.
Google CloudBigQueryApache Iceberg
Topic
Data infrastructure
Databases, pipelines, observability, quality and analytics platforms.
The value of data depends on the chain that makes it available and understandable. This topic covers databases, formats, pipelines and quality practices that make a platform reliable in production.
PostgreSQL 19 Beta 2: what to test before the final release
PostgreSQL 19 Beta 2 sharpens the changes expected this fall. Performance, observability and compatibility: a practical, low-risk test plan.
PostgreSQLdatabasemigration
Database backups: RPO, RTO and restoration testing
PostgreSQL, MySQL, Docker and PITR: an operational method for choosing backups, testing restoration and proving RPO/RTO targets.
backuprestorepostgresql
RAG architecture: build, evaluate and operate it in production
Ingestion, hybrid search, reranking, citations and metrics: a complete method for building a reliable RAG system and deciding whether it is production-ready.
ragarchitectureevaluation
Data architecture for AI: quality, RAG, pipelines and operations
From source to evaluation, the components required to supply an AI application with reliable, observable and governed data.
dataarchitecturerag
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
Apache Iceberg makes the lakehouse more credible
Iceberg brings snapshots, schema evolution and hidden partitioning to large analytical datasets.
lakehouseiceberganalytics
RAG and vector databases: quality starts with documents
Adding a vector database does not make an assistant reliable. Documents, chunks and metadata often determine the result.
ragvectorsai
Data quality: generative AI makes old problems more visible
Duplicates, incomplete fields and contradictory definitions become more dangerous when a model reformulates them confidently.
qualityaigovernance
Data observability: pipelines need traces too
Teams monitor APIs with traces and metrics. Data pipelines deserve the same rigor to understand delays and errors.
observabilitypipelinedata
Semantic layers stop revenue from being redefined in every dashboard
The semantic layer promises shared metrics across BI, notebooks and AI. It mostly requires clear governance.
analyticssemantic-layerbi
Mini PCs and homelabs: why developers are returning to local servers
Modern mini PCs make it possible to test Docker, databases and internal services without renting permanent cloud capacity.
mini-pchomelabserver
Product analytics without pushing dark patterns
Metrics guide decisions. Poorly chosen metrics encourage interfaces that manipulate instead of helping.
analyticsprivacyux
MLOps monitoring is more than server monitoring
Latency and availability are not enough. In production, a model can stay online while getting worse.
mlopsmonitoringai