Operations | Monitoring | ITSM | DevOps | Cloud

PostgreSQL is by design lightweight and un-opinionated but its killer feature has long been its extensions ecosystem. The extensions ecosystem adapts and customizes PostgreSQL data storage and manipulation use cases, making it suitable for AI, analytics, document data stores and more. This flexibility keeps PostgreSQL viable as an option for any business or startup, as it’s hard to ‘outgrow’ PostgreSQL.

Machine learning models must do more than make accurate predictions; they also need to adapt as the world around them changes. In real-world systems, data distributions shift due to seasonality, equipment wear, user behavior changes, or other external forces. If your models can’t keep up, the result is poor predictions. This can lead to outages, inefficiencies, or missed opportunities. That’s why forecasting systems need to be monitored and resilient, not just accurate.

KIP‑1150 - Diskless Topics is live. KIP-1150 Diskless Topics ship in Aiven Kafka BYOC on AWS, GCP, and Azure. Data goes straight to object storage—no local replicas, up to 80% lower TCO.

As a data platform engineer, you’re tasked with running complex workloads—Apache Spark jobs, AI/ML pipelines, batch ETL—across dynamic Kubernetes environments. Performance matters. Time spent tuning matters. And so does cost. But if you’re still relying on manual resource tuning to optimize your workloads, you’re playing a losing game. Sure, you can tweak CPU and memory requests by hand. You can comb through Prometheus metrics, look at job logs, estimate peaks.

As part of our big tent philosophy at Grafana Labs, we think you should be able to dig into your data and find meaningful insights — wherever that data happens to live. For many of our users, that data lives in Databricks, the open analytics platform for building, deploying, sharing, and maintaining enterprise-grade data, analytics, and AI solutions at scale.

InfluxDB 3 is our latest time series database built for real-time analytics and high-volume data. Its Python Processing Engine lets developers run custom scripts known as plugins to process data, trigger alerts, or integrate with external systems via HTTP web requests. To demonstrate what’s possible, we’ve developed several plugins, all of which are available in the influxdb3_plugins GitHub repository. This public repo is open for anyone to use, modify, and contribute to.

Cribl recently introduced Lakehouse, a powerful new feature within Cribl Lake that enables fast queries on the freshest data. But it’s so much more than just speedy searches. Lakehouse redefines how organizations collect, store, manage, and analyze telemetry data at scale, ensuring a future-proofed, cost-efficient, and flexible approach to data management.