For the last couple of years, Retrieval-Augmented Generation (RAG) architectures have become a rising trend for AI-based applications. Generally speaking, RAG offers a solution to some of the limitations in traditional generative AI models, such as accuracy and hallucinations, allowing companies to create more contextually relevant AI applications.

The Director of Product Management at AWS sat down with Fivetran to unpack how Iceberg and metadata are reshaping the future of data lakes, data warehouses and AI.

Selenium IDE is a popular open-source tool for recording and running automated browser tests. You can use it to interact with a browser the way a human would, doing things like navigating to URLs, filling in forms, and clicking on links and buttons. You can also use “assertions” to verify things are working as expected, like checking that elements are present, text is present, inputs contain certain values, et cetera.

The headless data architecture is an organic emergence of the separation of data storage, management, optimization, and access from the services that write, process, and query it. With this architecture, you can manage your data from a single logical location, including permissions, schema evolution, and table optimizations. And, to top it off, it makes regulatory compliance a lot simpler, because your data resides in one place, instead of being copied around to every processing engine that needs it.

Predictive machine learning continues to be a cornerstone of data-driven decision-making. However, as organizations accumulate more data in a wide variety of forms, and as modeling techniques continue to advance, the tasks of a data scientist and ML engineer are becoming increasingly complex. Oftentimes, more effort is spent on managing infrastructure, jumping through package management hurdles, and dealing with scalability issues than on actual model development.

From ancient Greek myths of talking statues to the modern-day Alexa and Siri, the concept of machines capable of understanding and responding to human language has captivated us for centuries. In recent years, this concept has evolved into AI chatbots, highly sophisticated tools that can read our queries and perform tasks ranging from customer service to automated alerts.

Epicor’s ability to provide industry-focused, scalable, and customizable ERP solutions has made it a popular choice for organizations across the globe. Epicor’s built-in reporting capabilities are useful for standard reports but can be limiting for organizations that require more advanced analytics. Without deep technical knowledge of Epicor’s data structures, attempting to manually create custom reports can create serious roadblocks to data trust within your organization.

GoReplay and Speedscale are two popular tools commonly used for load testing. Both share traffic shadowing as a core feature. This article compares both tools against the following criteria: For more Kubernetes load testing comparisons, check out our other posts.

Table of Contents In this article, we’ll compare two popular data integration platforms—Informatica and Integrate.io. We’ll explore the key differences between them, focusing on usability, integration capabilities, pricing, scalability, and customer support. By the end, you’ll have a clear understanding of which platform best suits your business’s data integration needs.

Retrieval-augmented generation (RAG) is gaining traction, and for good reason. As businesses and AI experts search for more intelligent ways to process information, RAG combines the best of both worlds, i.e., the vast knowledge of retrieval systems and the creative power of generation models. But what exactly is RAG, and why is everyone talking about it?