Systems | Development | Analytics | API | Testing

Microservices have gained a lot of traction in the last few years. Ever since the dawn of time, monolithism – packaging all your modules into one colossal codebase – was the default way to go when building software applications. However, as applications have had to cater to increasing volumes (of millions) of users over the internet over the last decade, scalability and flexibility have become ever more critical.

We constantly hear about UX Analytics. Analytics must always be a part of the design process of an app from the very beginning. We also hear tons about UI/UX design. However, and definitely during a redesign, UI and UX changes based on product analytics need to run parallel processes. So how come we never hear about UI Analytics?

State and local government agencies in the US are facing heightened demand to address a long-avoided issue: outdated technology. The existing solutions these government organizations rely on are not user friendly, limit collaboration by siloing essential data, and make processes longer and more cumbersome than they need to be. Yet, despite the limitations that result from outdated solutions, it should come as no surprise that only 60% of states have a digital strategy.

Would you like to learn to code and build popular websites or apps? Do you know the necessary steps to achieve this goal? Most people start by reading popular programming books or subscribing to online courses and tutorials that teach coding. While this approach may seem enjoyable at first, you’re likely to encounter confusing concepts and inadequate assistance quickly frustrates most newbie programmers.

API management is a system that provides a way for companies to manage, monitor, and analyze their APIs. It helps them keep track of how these APIs are being used internally and externally to understand better how to improve them. In this post, we'll talk about API management and why your business needs to use it.

A REST API — or representational state transfer application programming interface — is a data interface that allows for the creation and consumption of resources. It’s not just for GET requests, but also for other HTTP verbs such as PUT, POST, and DELETE. REST APIs can also handle CRUD (Create, Read, and Update, and Delete) operations. In this way, a REST API is a software bridge between two levels of abstraction in a system.

Applications that use a REST API architecture are designed to be loosely coupled and transfer information quickly. While this architecture focuses on transferring data between clients/servers, it can also apply when interacting with other systems and users. In the world of app development, the need for better, faster, and easier-to-understand APIs is growing, and REST APIs are dominating the legacy modernization conversation.

With more data and systems at their fingertips than ever before, organizations need a concrete strategy for how to connect them with their own enterprise IT. This has led to the rise of API-led connectivity, which offers a dramatic shake-up to the way that companies access and integrate these technologies and services. In this article, we’ll explore five industries in which API-led connectivity has changed the business landscape as we know it.

With the general availability of Cloudera DataFlow for the Public Cloud (CDF-PC), our customers can now self-serve deployments of Apache NiFi data flows on Kubernetes clusters in a cost effective way providing auto scaling, resource isolation and monitoring with KPI-based alerting. You can find more information in this release announcement blog post and in this technical deep dive blog post. Any customer willing to run NiFi flows efficiently at scale should now consider adopting CDF-PC.

At Snowday 2021, Snowflake announced exciting new product capabilities that expand what is possible in the Data Cloud. In addition to announcing Python support in Snowpark (currently in private preview), these latest innovations make it easier for organizations to maintain business continuity across clouds and regions; help data engineers and data scientists build pipelines, ML workflows, and data applications faster; and remove the complexity of getting the right data into the hands of customers.