Operations | Monitoring | ITSM | DevOps | Cloud

The latest News and Information on Databases and related technologies.

Find Your PostgreSQL Connection URL

In this video, we show you how to quickly connect to your Aiven for PostgreSQL instance by locating your service URL in the Aiven Console. Your login credentials remain secure but can be copied easily, and we demonstrate how to connect using a variety of tools, including the Aiven CLI, PostgreSQL clients, and popular programming language modules. Get up and running in minutes and start interacting with your database effortlessly, whether for development, testing, or analytics.

Add Postgres with one YAML line. Deploy in under a minute.

Let’s break down why this matters, and how it can change the way you approach building and running applications. You want database power without getting bogged down in tooling and config. Most of your week should be building features, not hunting for connection strings or maintaining bespoke infra scripts. Developers tell us they just want to code and solve application problems, with minimal platform friction.

Building dbRosetta Using AI: Part 3, Creating a Database

The AI said I had to do a database first, not code. Who am I to argue? So, with all the prompts outlining the goals of the project, I’ve gone forward with the project, and step one is creating a PostgreSQL database on Azure. This is part three of a multi-part set of articles. I’ll move this list to the bottom of future articles: Part 1: Introducing the Concept of dbRosetta Part 2: Defining the Project & Prompt Templates.

Devart ODBC Drivers vs Free ODBC and JDBC: Key Comparison

Most teams never question the JDBC or ODBC drivers they use. If it connects, it’s “good enough.” That assumption can cost more than $14,000 per minute during an outage, according to EMA’s 2024 IT downtime benchmark. Drivers are more than connectors. They dictate how efficiently data moves between databases, applications, and analytics tools. When overlooked, the entire stack slows down. Breakdowns at this level lead to failed reports, missed deadlines, and avoidable downtime.

AI Agents Observability with OpenTelemetry and the VictoriaMetrics Stack

Nowadays, AI agents are becoming more and more popular and often deployed as part of production systems. However, this rapid adoption brings unique observability challenges that require flexible solutions. On the one hand, AI agents are fundamentally just like any other software services that produce the same classic observability signals we’re familiar with: metrics, logs, and traces.

Simple Talk Podcast - Coffee Chat with John Sterrett

Simple Talk Podcast – Coffee Chat with John Sterrett Description: Steve chats with John Sterrett, CEO of ProcureSQL, about his true love for data from a young age, how SQL Saturday and community events inspired him to start his own company, ProcureSQL’s use of AI to provide more value, and the impacts of work on relationships - plus much more!

Replication Job Monitoring Support in Redgate Monitor

Whether it’s a stalled Log Reader Agent, a conflicting insert on the subscriber, or a failed cleanup job bloating the distribution database, Redgate Monitor now brings SQL Server replication issues to light early, before performance or reliability are affected. In many SQL Server environments, replication remains essential for offloading reporting and analytics workloads, or for maintaining local and synchronized data copies across regions.

Building dbRosetta Using AI: Part 2, Defining the Project & Prompt Templates

This is the next installment of the series on building a database and an application called dbRosetta using AI/LLM. Part 1 introduces the concept. THE AI PICKED DATABASE FIRST! Look, I talk databases at this thing a lot, so it probably knows my own preference, but when I asked it, it chose to build a database separate from the code. Let’s get into it.

Unleashing Powerful Analytics: Technical Deep Dive into Cassandra-Spark Integration

Apache Cassandra has long been favored by organizations dealing with large volumes of data that require distributed storage and processing capabilities. Its decentralized architecture and tunable consistency levels make it ideal for handling massive datasets across multiple nodes with minimal latency. On the other hand, Apache Spark excels in processing and analyzing data in-memory, making it an excellent complement to Cassandra for performing real-time analytics and batch processing tasks.