According to Statista, the microservices architecture market will grow at a robust CAGR of 19.6%, reaching $10.86 billion by 2027, up from $4.44 billion in 2022. The shift to microservices architecture has revolutionized software design and development, significantly enhancing agility, scalability, resilience, delivery times, cost-effectiveness, and more. Since Dr.

except Exception as e is a construct in Python used for exception handling. It allows you to catch exceptions that occur during the execution of a block of code by using a try block to wrap the code that might raise an exception, and an except block to catch and handle the exception. The Exception part specifies that any exception of this type or its subclasses should be caught, and the as e part assigns the caught exception to a variable e, which you can then use to access details about the exception.

As automation capabilities grow, so do the related use cases. It’s not surprising that 80% of executives think automation can be applied to any business decision, according to a Gartner survey. But as implementation becomes easier and automation adoption more prevalent, your organization can quickly lose control, with too many automation projects happening in silos.

When two different software applications want to join forces by sharing information, it’s not always a perfect union. Different database structures, programming languages, and network protocols tend to complicate the process. When reading about the various API technologies, you’ve probably come across two terms, OData vs REST, and wondered, “what’s the difference?” It turns out that OData vs. REST are two closely related concepts.

In the fast-evolving landscape of data science and machine learning, efficiency is not just desirable—it’s essential. Imagine a world where every data practitioner, from seasoned data scientists to budding developers, has an intelligent assistant at their fingertips. This assistant doesn’t just automate mundane tasks but understands the intricacies of your workflows, anticipates your needs, and dramatically enhances your productivity at every turn.

Enterprises are beginning to implement gen AI across use cases, realizing its enormous potential to deliver value. Since we are all charting new technological waters, being mindful of recommended strategies, pitfalls to avoid and lessons learned can assist with the process and help drive business impact and productivity. In this blog post, we provide a number of frameworks that can help enterprises effectively implement and scale gen AI while avoiding risk.

In this post, we demonstrate how a robust and scalable generative artificial intelligence (GenAI) chatbot is built using Amazon Bedrock and Confluent Cloud. We walk through the architecture and implementation of this generative AI chatbot, and see how it uses Confluent's real-time event streaming capabilities along with Amazon's infrastructure to continually stay up to date with the latest advances from the AI landscape.

If you think workflow orchestration sounds like tech jargon, check out this simple explanation and consider practical advice for how to apply it to your business processes. Let’s start with the basics: what is workflow orchestration? Put simply, workflow orchestration is the end-to-end management of people, digital workers, systems, and data in a process.