The traditional definition of API monitoring has been around for years from companies like SmartBear, APIMetrics, and Runscope and useful to check API correctness and performance. An API monitoring tool initiates API calls against your chosen endpoints and then records the response received. Additional checks can be added such as create a slack alert on a 500 error or timeout.

You know the feeling. You’ve just deployed a new version to production and are monitoring the Rollbar dashboard for any new errors or looking out for any Slack notifications. You’re keeping an eye on the number of new or reactivated errors, their severity level, and triaging to see which errors need to be assigned and to whom. Now what if you could automate these tasks?

For many companies, cloud costs are among the top investments these days. With a growing number of services, instances and regions, cloud cost optimization is becoming increasingly painful. Companies use cloud management platforms to optimize costs and increase cloud visibility and security. But staying on top of AWS budgets requires proficiency, agility and time—especially when any glitch can result in massive cost bleeds.

Cloud for analytics may be the biggest bait and switch in recent history. Until now. Not long ago, cloud was billed as the promised land – a mystical paradise of flexibility, scalability, performance and, best of all, lower costs than we ever thought possible.

APIs are a critical component of any enterprise’s digital transformation strategy. They can drive customer engagement, accelerate time to market of new services, power innovation and unlock new business opportunities. Therefore, choosing the right API management platform is critical to running a successful API program, and research from industry analyst firms like Forrester Research can help enterprises evaluate and choose the right solution.

We are happy to announce the general availability of Kong Mesh 1.0! Kong Mesh is Kong’s first enterprise service mesh built on top of Kuma and Envoy that organizations can use to deploy a modern mesh across every application — running on both Kubernetes and VMs — and on every cloud or data-center.

Few things are more frustrating than slow, flaky test suites. You're ready to deploy, wait 20 minutes for CI to run, only to find that a test failure in code you've never touched is blocking you. You dig into the source and find the problem: an external API call. It works (slowly) most of the time. But sometimes the network glitches and it fails. What do you do? In this article, José Manuel shows us several techniques for removing external API dependencies from our tests.

Appian, in cooperation with Defense Information Systems Agency (DISA) engineers and the United States Marine Corps, now supports mobile access to Appian applications within the Department of Defense Information Network (DODIN). This enables access to the Appian platform, as well as Appian applications and solutions deployed in a DODIN Cloud-approved enclave (AWS, GovCloud, Azure, etc.) via any DOD-approved iOS or Android mobile device.

“Every organization — no matter how big or how small — needs data quality,” says Gartner in its newly published Magic Quadrant for Data Quality Solutions. However, with more and more data coming from more and more sources, it’s increasingly harder for data professionals to transform the growing data chaos into trusted and valuable data assets.

Today’s public sector organizations and government agencies demand a new standard for communicating and sharing information. That includes data-rich content that moves through environments, networks and locales. From being stored, analyzed and shared, to quickly and effectively moving between environments, to spinning up in clusters and informing endless applications—data is more critical than ever.