Systems | Development | Analytics | API | Testing

When Snowflake acquired the TruEra AI Observability platform, we committed to keeping TruLens open source. We’re not only keeping that promise; we’re emphasizing it. Our goal remains to support LLM app developers in creating trustworthy generative AI applications. In the weeks since the acquisition, we have already added ecosystem-friendly enhancements including: We plan to continue making enhancements and improvements that benefit the community at large, whether on Snowflake or not.

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.

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.

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.

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.

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.

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.

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.

The 2024 Gartner Magic Quadrant for Analytics and BI Platforms just dropped, and we’re thrilled to announce that ThoughtSpot was recognized as a Leader in the report. But, we aren’t the only ones finding ourselves in a new position this year. The analytics and BI space has undergone some of the most significant shifts in over a decade, an aftershock of generative AI.