Operations | Monitoring | ITSM | DevOps | Cloud

For a long time, talking about Digital Employee Experience (DEX) inside the company was almost synonymous with “making the computer faster” or “reducing support tickets.” Today, that view is limited. Digital Employee Experience is now treated as a direct lever for productivity, talent retention, and business results—not just as an operational IT concern.

Sizing infrastructure has always required a balance between performance, capacity, and risk. What has changed is the level of precision required to make those decisions. Hardware timelines are less predictable. Costs are under closer review. Decisions that were once routine now require clear justification. In many cases, the question is no longer just how much capacity is needed, but whether that capacity can be delivered when it is needed and whether the investment will hold up under scrutiny.

Three Exchange Online mailbox types admins constantly confuse - what each one is for, the licence rules, and which to use when an employee leaves.

Engineering teams lose hours in the gap between detecting a problem and getting a fix into review. An on-call engineer sees an error spike in Datadog, pivots to traces and logs to isolate the failure, opens the relevant repository, reproduces the issue, writes a fix, adds tests, waits on CI, and finally opens a pull request. Even when the problem is familiar, the workflow pulls engineers across several tools and stretches remediation from minutes into hours or days.

A challenge for many teams continues to be managing cost, governance, and reliability across an ever-larger footprint. This year’s DASH announcements help teams operate efficiently at scale, with new tools to cut cloud and AI spend, eliminate waste automatically, maintain observability during outages, and manage many organizations and agents as a single unit.

Kubernetes dashboards often mask a systemic infrastructure failure. When a critical Java service fluctuates and restarts, the post-mortem often confirms an Out-of-Memory (OOM) event. While CPU metrics appear healthy, memory has silently hit a ceiling, forcing the kernel to terminate the process.

Monitoring is built around the system a team understands at a point in time. Engineers add endpoints, move dependencies, and change user flows every day. Over time, that creates coverage drift as monitors keep reflecting the system as it used to behave, while changing paths introduce failure modes that teams didn’t yet know to watch for. Bits Detection automatically creates, tunes, and maintains monitors for your services.

Teams are wiring AI coding agents straight to their warehouse over MCP and asking things like “What was our revenue by channel in Q2?” The agent finds a revenue table, runs a query, and returns a number in seconds, with no waiting on the data team. While the answer initially looks right, the problem is that the number is often wrong.

In this article we’ll show how we built an image search demo in Aiven Apps. The demo uses the CLIP Large Multimodal Model (LMM) to turn a user’s text prompts into a vector that can be compared with the precomputed vectors for a corpus of images, allowing the user to find images based on their text. While in this example the LMM input (the text prompt) is coming from the user, the principle is the same as for an internally generated query.

Aiven for Valkey includes the Valkey Search module setup and ready to go. Here's what that looks like in practice: a small online shop adding real search on top of the cache it's already running. Needle & Yarn sells the yarn you crochet with (skeins) and the design patterns you crochet from. Like a lot of e-commerce backends, it already runs Valkey as a product cache, with each product stored as a Hash for hot-path performance.