Operations | Monitoring | ITSM | DevOps | Cloud

Java is one of the most widely used platforms for supporting web applications. According to RedMonk and TIOBE rankings, Java has consistently remained in the top 4 most popular programming languages worldwide, with millions of developers actively using it. Industry-standard application servers such as WebLogic, WebSphere, Tomcat, and JBoss all run on Java and power a large share of enterprise workloads and Java web applications.

Recently, crowdfunding giant GoFundMe made headlines after (among other things) automatically generating some 1.4 million donation pages for U.S. 501(c)(3) nonprofit organisations. These pages were created without prior consent, using publicly available IRS data and partner-feeds. According to reports, many nonprofits discovered these pages only when alerted by a donor or curious patron; they had no advance knowledge and had to manually un-publish or “claim” the page.

As a technology leader at Elastic, my team is responsible for building AI experiences on a centralized architecture to enable our sales and customer support teams to scale their impact and take advantage of AI faster.

With increasing security threats and stringent compliance requirements, database code quality isn’t just a best practice; it’s a business imperative. Yet many organizations struggle to enforce their database development standards consistently across teams, leading to security vulnerabilities, potential data loss, and lengthy review cycles that slow down software delivery.

Every finance team knows this pain. The cloud bill continues to grow, but the numbers don’t quite add up. Engineering swears their usage reports are accurate. But when you ask, “Which product or customer drove last month’s 18% cost increase?” things go quiet. That silence usually means one thing. Inaccurate cost allocation. Native cloud cost allocation tools from AWS, Azure, and Google Cloud can help. However, they often stop at the average this or average that layer.

SaaS, AI, and technology spending today looks like a more intense version of how it was a decade ago when everyone first migrated to the cloud. The mentality was and, at some companies, still is to build build build and worry about controlling expenditures and optimizing costs later. We’re seeing astronomical amounts of money being raised by brand-new AI businesses that barely even existed a couple of years ago. More than $145 billion has been raised for U.S.

At 2:07 a.m., one of the core production nodes went down. CPU usage spiked, latency shot through the roof, and requests began timing out across the cluster. Monitoring tools lit up instantly. Datadog dashboards turned red, Prometheus fired alerts, and a webhook pushed incident payloads into Jira. Everything worked exactly as designed. Except no one responded. The alert chain fired flawlessly through machines, but the right human never saw it because it was sent via an automated phone call.

In part one of this series, we learned how to implement batch updates and advanced inserts in Ecto to dramatically improve database performance. But implementing these optimizations is only the first step. Ensuring they continue to work effectively in production requires professional monitoring and observability. This guide will show you how to use AppSignal for Elixir to monitor your Ecto application's performance when dealing with batch data operations.

The speed and reliability of deploying backend and full-stack applications are usually a concern for development teams. Fortunately, Railway is a developer-friendly platform that allows you to deploy apps with limited configuration. It is also quick, easy to use, and has reasonable defaults. Now, imagine pairing that with CircleCI, one of the strongest continuous integration platforms available.

If you're anything like your peers, you probably blinked in April and found yourself a month away from Black Friday when you opened your eyes. Much like a shopper desperately scrambling to pull together gift lists for their loved ones, many engineering teams find themselves rushing to ensure their systems can handle the biggest shopping day of the year.