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.

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.

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.

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.

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.

One of the new features of Xcode 16 is called "explicitly built modules". Behind this abstract name is something that makes builds faster and compiler errors more informative. As this is enabled by default for C and Objective-C code, you can experience some of the benefits instantly, but it can also be enabled for Swift code as an experimental feature. In this post, we'll explore how this feature works and the benefits it brings to projects that adopt it.

As programmers we know that, despite our best efforts, we’ll never be able to completely eliminate errors from our apps. The sheer complexity of modern apps, not least the reliance on dynamic (often third-party) inputs, means errors are inevitable and error handling (exception handling) is crucial to user experience.

How to use BigQuery vector search to analyze logs and asset metadata stored in BigQuery tables.