Systems | Development | Analytics | API | Testing

Analytics and data visualization are essential parts of modern business intelligence (BI). These tools help you better understand your business and improve overall decision-making. However, as your data grows and your users' analysis needs evolve, it may be time to upgrade your visualization solution to achieve better capability and reporting.

One of the most significant benefits of the modern data stack is the loosely coupled nature of each layer to help you adapt to change and capitalize on new business opportunities. You can choose the best solution which fits your need without long-term vendor commitments, and the risk of introducing complex integrations and IT management. One of the ways to achieve this loose coupling is through webhooks.

Understanding the stages of API lifecycle management offers an overhead look at application programming interfaces so you can find opportunities for improvement. Below, you will find the three major stages of an API lifecycle. Each section offers a closer look at the steps professionals often address when optimizing API strategy, functionality, access control, workflows, and other critical features.

In an ideal world, you want to have precisely the capacity to manage the requests of your users, from peak periods to off-peak hours. If you need three servers to attend to all the requests at peak periods and just one server at off-peak hours, running three servers all the time is going to drive up expenses, and running just one server all the time is going to mean that during peak periods, your systems will be overwhelmed and some clients will be denied service.

One morning, you realize you have a great idea for an API. You discuss it with your team, then start building out the business case and technical requirements. Where do you go from there? You could write out the business requirements for the API and then code it. Or you could describe your API in a specification language, like OpenAPI, and use that definition to improve your team's understanding of the API and do some early testing. But are either of these the best solution?

Choosing the right tool for a web application can be tricky. But if you do, it will make things a lot easier for you. Depending on your application, combining Node.js and MongoDB works well most of the time — especially if you use a front-end framework like React (MERN), Angular (MEAN), or Vue (MEVN). In this tutorial, you will learn how to create a CRUD application using Node.js and MongoDB and write some basic tests for your Node.js API.

What is an actionable dashboard, and how do you create one? Learn how to build a dashboard that drives you to meet your business goals.

With the introduction of new application platforms like Kubernetes, oftentimes the DevOps tooling around it needs to evolve. Cloud Native technology is powerful but complex. This 5 minute demo video shows how Speedscale provides production simulation capabilities so you can check for resiliency, quality and scalability in your Kubernetes clusters. You can record data and traffic in production and replay sanitized traffic on the fly against a new cluster.

Oracle Cloud ERP enables businesses to harness the power of the cloud with built-in security, easy access to data, and native reporting tools. Offering scalability, security, and greater visibility into your organization’s information, this ERP comes with a variety of benefits. But when you’re looking to transition into a cloud-based ERP, where do you start? Here, we discuss the top five best practices of moving to Oracle Cloud ERP.

It is pretty common to envision software developers spending most of their time writing fancy code, building new, shiny features for applications, and smoothly deploying them to production. However, the reality is usually messier. Things break all the time — your code fails, the system runs out of memory, exceptions go unhandled, dependencies conflict, servers overload, slow requests take forever, and whatnot.