Learn how the Fivetran Analytics team uses natural language processing for rationalizing and addressing customer error messages.

Two related concepts often come up when data teams work on data governance: data provenance and data lineage. While they may seem similar at first glance, there are fundamental differences between the two concepts. Data provenance covers the origin and history of data, including its creation and modifications. On the other hand, data lineage tracks the data’s journey through various systems and processes, highlighting its flow and transformation across different data pipeline stages.

In this post we are going to look at the importance of volume testing. We are going to consider how this type of non-functional test can have a significant impact on your size and scale your production environment based on evidence this test provides.

Local governments aim to serve the needs of their communities by fostering a safe, prosperous, and vibrant environment where residents and businesses can thrive. But too often, inefficient processes and manual, paper-based tasks consume valuable time and resources. This leads to delays, errors, and inconsistencies in the delivery of essential services, eroding citizen satisfaction and trust.

Jeremy H - May 20, 2024 The terms RESTful API and Microservices go hand-in-hand when building a microservices-based application. Nevertheless, they refer to very different things. The easiest way to understand the difference between RESTful APIs and microservices is like this: Table of Contents.

The capacity to comprehend a system’s or application’s internal state and behavior based on its external outputs or events is called observability. In other words, it’s the capacity to extrapolate a system’s current state from its observable output without directly accessing its internal workings. Observability is crucial in disciplines like software engineering, systems engineering, and control theory.

AI applications that produce human-like text, such as chatbots, virtual assistants, language translation, text generation, and more, are built on top of Large Language Models (LLMs). If you are deploying LLMs in production-grade applications, you might have faced some of the performance challenges with running these models. You might have also considered optimizing your deployment with an LLM inference engine or server.