Operations | Monitoring | ITSM | DevOps | Cloud

Pastries with SREs: From AIOps to GenAI and LLMs (lactose-free latte making)

In this episode of Pastries with SREs, we look at AIOps, where it fell short, where it worked, and how generative AI (GenAI) is reshaping what’s possible in observability today. We explore: If you’re wondering whether generative AI is different this time, this episode offers a grounded, practical look at how it’s evolving observability workflows.

I Benchmarked EBS vs. S3 Express So You Don't Have To - Here's What I Found

I benchmarked EBS and S3 Express One Zone across EC2 and EKS setups to settle a question we kept hearing from customers: “What’s the best temp storage for ML pipelines?” The short answer: Here’s the full story and why it matters to your ML pipelines.

What Are AI Guardrails

When you're shipping LLM features, a lot of the work goes into keeping the model's behavior predictable. You deal with questions like: These are everyday concerns when you integrate LLMs into production systems. Guardrails AI provides a Python framework that helps you enforce those expectations. You define the schema or constraints you need, and the framework validates both the inputs going into the model and the outputs coming back.

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.

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.

Top 10 APM Tools [2026 Guide]

In 2026, application performance isn’t just a technical metric—it’s a business-critical factor. As organizations move deeper into cloud-native architectures, distributed systems, and AI-driven workflows, ensuring speed, reliability, and uptime has become non-negotiable. According to Gartner, by 2026 more than 70% of new APM implementations will be cloud-native, and businesses that leverage advanced observability platforms are expected to reduce downtime by up to 60%.

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.