Systems | Development | Analytics | API | Testing

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.

A few weeks ago, Apple hosted its annual developer conference WWDC 2024, and made many announcements that will impact the mobile developer ecosystem. ‍ In a previous blog, we outlined the three top high-level announcements. In this post, we are diving into what’s new for two very important components of iOS- Xcode 16 and the App Store Connect.

Present in every version of Java, the java.lang.RuntimeException is an essential class that allows your application to handle unexpected problems without crashing. Runtime exceptions are exceptions only detected during the execution of your app - things like invalid user input or issues with external resources like files or networks. When an unexpected situation like those occurs, a RuntimeException can be thrown and your app can catch and handle it using a try-catch block.

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.

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.

When creating an ODBC driver, it is essential to convert data from the data source into a relational format. This is necessary because ODBC and SQL require and operate on data that is organized with a fixed schema and returned in a tabular structure.

Testing is an essential part of any software deployment project, whether a new eCommerce gateway or migrating from SAP ECC to S/4HANA. But ERP testing SAP and other packaged applications differs from standard web or mobile application testing.

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.

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.

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.