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The philosophy behind continuous improvement is simple: incremental and ongoing changes to your organization result in more efficient business processes, better products, and superior customer experiences. The pursuit of excellence is familiar to many of us in our daily work, and your organization may already have a culture, goal, or process management method designed to improve performance and facilitate operational excellence.

Today, data is the lifeblood of every organization. Without it, you’re left without key insights into business processes, consumers, employees, and the overall health of your entire organization. And here’s the scariest part: most organizations are on life support due to their inability to properly manage their data and turn it into actionable insights. Data fabric technology can help in a significant way, which is why it’s been getting so much buzz recently.

Our next-generation API management platform built on a Kubernetes-native architecture on top of Envoy Proxy

Ractor is Ruby's new Actor-like concurrency abstraction—it lets execute code in parallel without worrying about thread safety. This article is an excellent introduction to Ractors and how to begin using them in your Ruby code.

Data fabrics are getting a lot of attention lately, and for good reason. But, for any topic with a lot of hype, there also tends to be a lot of confusion. If you are still trying to fully grasp where the concept of a data fabric architecture fits amongst all of the warehouses, lakes, lakehouses, and meshes of the data engineering world, let's set the record straight. What is a data fabric? A data fabric is a toolset that connects data across disparate sources to create a unified data model.

Python is a versatile and powerful programming language that has become increasingly popular over the years. One of its most important features is the ability to define and use functions. Functions are a fundamental building block of any program, allowing us to encapsulate a piece of code that can be called and reused multiple times. In other words, functions are like mini-programs that can be called from within our main program.

Artificial intelligence (AI) and machine learning (ML) bring significant efficiency gains to organizations and should play critical roles in any digital transformation efforts. One major use case for these technologies is AI document processing, also known as intelligent document processing (IDP). Organizations amass a large number of business documents—from invoices in billing departments to medical coding documents at hospitals.

Why do experiences matter? In turbulent economic times, organizations that can't offer high-quality digital products and services with excellent experiences cannot attract and retain customers and employees. Customers today are seeking more than just products or services. They are looking for memorable experiences that create a lasting impact and emotional connection with the organizations they interact with. And they are more than willing to pay more to have those experiences.

Digital transformation has been a buzzword for quite some time, but the COVID-19 pandemic has accelerated the need for organizations to adopt digital solutions. As we enter a new year, it’s worth taking a closer look at the top digital transformation trends that will shape business strategies in 2023. We’ll explore six key trends that will help organizations stay ahead of the curve and achieve digital transformation success.

As more organizations are moving to a cloud-native architecture, there is an ever-increasing need to monitor applications and services. Logging is a crucial part of this process, as it provides the insights and visibility to identify potential issues and track application performance. When it comes to managing and monitoring applications, Docker logging is an essential part of the process.