Not until the early did the concept of a container-based application emerge to transform the IT world. For the first time, the software was deployed consistently and reliably regardless of the target environment’s changes (private or public cloud, personal computer, etc.). With the development of containers, container orchestration has become a trending topic in recent years, with successful applications from the likes of tech giants Facebook, Google, Netflix, among others.

When you’re providing APIs to your customers, you want to ensure they are getting value from them. At the same time, the best APIs are designed to be fully automated without requiring human intervention. This can leave your customers in the dark on whether your API is even being used by the organization and if you’re meeting any SLA obligations in your enterprise contracts.

When building a production application, you are usually on the lookout for ways to optimize its performance while keeping any possible trade-offs in mind. In this post, we’ll take a look at an approach that can give you a quick win when it comes to improving the way your Node.js apps handle the workload. An instance of Node.js runs in a single thread which means that on a multi-core system (which most computers are these days), not all cores will be utilized by the app.

One of the reasons we selected Sierra Ventures as one of our seed investors is because of their CXO Advisory Board. They have dozens of knowledgeable advisors across a wide variety of verticals: Healthcare, Consumer, Retail, Finance, Technology, Media and Telecom. Each year they conduct a broad survey of major trends. This kind of survey data is gold for Enterprise-focused startups.

Businesses today have a growing demand for real-time data integration, analysis, and action. More often than not, the valuable data driving these actions—transactional and operational data—is stored either on-prem or in public clouds in traditional relational databases that aren’t suitable for continuous analytics.

Continuous evaluation—the process of ensuring a production machine learning model is still performing well on new data—is an essential part in any ML workflow. Performing continuous evaluation can help you catch model drift, a phenomenon that occurs when the data used to train your model no longer reflects the current environment.

The data lakes concept has come back into popular focus with Amazon Athena, an innovative, serverless solution. But does it fit into your organization’s data stack? This article covers Amazon Athena capabilities, pros and cons, competitors, and use cases.

Conventional wisdom says that new app development projects should begin as monolithic systems. This perspective suggests that you should wait until later to refactor the monolith into a microservices-based architecture — i.e., don’t use microservices until your use-case and scaling demands require them. But is this viewpoint still correct?

COVID-19 vaccines from various manufacturers are being approved by more countries, but that doesn’t mean that they will be available at your local pharmacy or mass vaccination centers anytime soon. Creating, scaling-up and manufacturing the vaccine is just the first step, now the world needs to coordinate an incredible and complex supply chain system to deliver more vaccines to more places than ever before.

Innovative organizations need DataOps and new technologies because old-school data integration is no longer sufficient. The traditional approach creates monolithic, set-in-concrete data pipelines that can’t convert data into insights quickly enough to keep pace with business. The following trends are driving the adoption of Hitachi’s Lumada DataOps Suite.