Operations | Monitoring | ITSM | DevOps | Cloud

Change Data Capture (CDC) is a technique used to identify and capture changes, such as inserts, updates, and deletes, in a source database so they can be replicated to another system in real-time. This approach is crucial in modern data pipelines, especially for powering data lakes, analytics platforms, and event-driven applications that depend on up-to-date information. Setting up a CDC pipeline is only the first step.

With increasing cyberattacks and cloud outages, maintaining system resilience is critical. A robust Disaster Recovery (DR) strategy enables teams to prepare for unexpected events. It makes sure they can recover critical systems and data with minimal disruption. This blog will cover what disaster recovery is, why it matters, and the key components of an effective Disaster Recovery Plan. We’ll also walk through the steps for creating your own strategy.

A well-structured server infrastructure is not just about having the right hardware in place. It is about creating a strong foundation that ensures performance, security, and reliability. Poorly designed infrastructure can lead to downtime, security breaches, and reduced efficiency. On the other hand, an optimized setup empowers businesses to scale, innovate, and meet customer demands effectively.

Large enterprises face the challenge of managing high-volume, very diverse data streams that span both legacy and modern, digital systems and applications. To gain timely, accurate insight across this kind of complexity, IT teams need observability platforms that can do more than just monitor - they must also unify, contextualize and enrich data so teams can act effectively to protect the availability of the services their customers rely on.

The Distinct Value Cache (DVC) available with InfluxDB 3 Core and InfluxDB 3 Enterprise lets you cache distinct values of specific columns and query those values in under 30ms. The DVC is an in-memory cache that stores distinct values of one or more columns in a table. It is typically used to cache distinct tag values, but you can also cache distinct field values.

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Platform teams handle cluster provisioning manually because it requires oversight. A product team needs a cluster, so they submit a request. Someone reviews the configuration, provisions it through the cloud provider, waits for it to spin up, and sends back access details. Two days later, the cluster is ready. The delay is intentional. Without review, teams provision oversized clusters that blow budgets, misconfigure networking, or deploy in the wrong regions.

Security data is exploding. Most organizations see their telemetry volumes double every two to three years, driven by cloud adoption, distributed architectures, and an expanding attack surface. Yet only a small percentage of that data contains real indicators of compromise. Analysts estimate that nearly 80 percent of SIEM and observability logs have little or no analytical value.

Last month, we launched AI Code Review, our developer tool that automatically catches bugs, finds performance issues, and helps you ship PRs faster. 30 days later, here's what’s new.

ilert’s engineering team has developed an open Model Context Protocol (MCP) server that enables AI assistants to securely interact with your alerting and incident management workflows, from determining who is on call to creating incidents. In this article, we provide a simple explanation of MCP, outline the reasons behind our investment in it, describe the high-level architecture, and explain how to connect Claude, Cursor, and other MCP clients to ilert today.