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Learn how to safely automate Oracle schema changes. Discover what makes Oracle database DevOps unique. Automating Oracle schema changes safely means considering what makes Oracle different. A real Oracle database DevOps practice pairs version-controlled changelogs and pipeline integration with policy-as-code protection, pre-flight checks, and tested rollback scripts, so schema changes deploy with the same speed and safety as application code.

The conversation around sovereign cloud in India is getting louder, which is welcome, but as more providers enter the space, I keep seeing the same architectural pattern, and it's worth being direct about what it misses. The pattern is familiar... take a cloud platform, host it in India, manage it end to end, call it sovereign, and let data residency carry the rest of the argument. That works for web apps and standard enterprise workloads.

Until now, someone on your team had to start every root cause analysis in Rollbar. An alert fires, they open the item, read enough of the stack trace to decide it deserves an investigation, and start the analysis by hand, after dropping whatever they were doing. With Agent Automation, Resolve, Rollbar’s AI agent for root cause analysis and fixes, takes over that step. You write a rule once in Project Settings, and when an error matches it, Resolve starts the analysis on its own.

When profits miss plan, marketing is the budget line most likely to be cut. The CMO Survey found that marketing expenses are cut 45.4% of the time in that scenario, more often than any other expense category. Most marketing teams have more data than ever. Finance still discounts much of it, because few of those numbers connect spend to business outcomes. The pressure is structural. Gartner’s 2026 CMO Spend Survey shows marketing budgets flat at 7.8% of company revenue.

The first CRA deadline is behind us. Now comes the opportunity to solve the engineering problem underneath it. When I worked with connected appliances at Electrolux, I learned something that is obvious to customers but sometimes less obvious to product organizations: Release day is not the end of product development. In many ways, it is the beginning of the longest and most unpredictable part of the product lifecycle.

The model your team relies on today is unlikely to be the one you're relying on a year from now. If your team's process for shipping AI-assisted code is built around a specific model, coding assistant, or a vendor's take on an autonomous agent, you are not building infrastructure. You are building something you will tear out and rebuild the next time the leaderboard shifts.

The phrase “AIOps automation” now appears across observability platforms, ITSM suites, orchestration products, runbook tools, and general automation software. The label alone says little about what happens after detection. Some products create a ticket. Some launch a script. Others gather live context, choose an approved response path, execute across several systems, verify recovery, and update the operational record.

The digital workplace has outgrown the support model built to maintain it. Employees now depend on an mix of devices, applications, AI tools, and connected workflows to get their work done. As this environment becomes more complex, IT is expected to support more technology, control costs, and enable greater productivity. Traditional, ticket-driven support makes that difficult.

Most of the time, when we read a file, we do not think much about what happens underneath. You ask for some bytes, you get some bytes. But when getting data off the disk efficiently is at the core of what your application does, what happens underneath starts to matter a great deal, and two identical read() calls can differ enormously in what they cost. So in this post we are going to look at one of the Linux kernel’s optimizations for reading files: readahead.

If you’re running FastAPI, you’re running Starlette underneath it, whether you think about it or not. At Scout, we spend a lot of time thinking about what happens at that ASGI layer, since that’s exactly where our Python agent hooks in to give you request-level visibility. Starlette 1.7.0 just landed, and it’s a release worth reading closely if you care about tracing, routing, or just keeping your app running on a supported dependency chain.