If you’re working in the data space today, you must have felt the wave of artificial intelligence (AI) innovation reshaping how we manage and access information. One of the areas affected is data catalogs, which are no longer simple tools for organizing metadata. They’ve evolved dramatically into powerful, intelligent systems capable of understanding data on a much deeper level.

Learn how CHS Inc., Pitney Bowes and Envision Pharma use Fivetran for high-volume database replication to streamline operations, drive growth and save money.

In today’s digital landscape, maintaining a smooth user experience is essential. Websites and applications are expected to perform reliably and quickly, with minimal interruptions. One effective approach to enhancing user experience is synthetic monitoring, which uses simulated transactions to test performance. This article will delve into how synthetic monitoring improves user experience and highlight its benefits, along with code examples for implementation.

Containerized microservices are essential to cloud migration and digital transformation plans. Do you have a clear picture of what containerized microservices are and why they’re important? In this guide, you'll learn about containers, microservices, and how they work together. First up is a bit of history that explains how the need for containers came to be. Here's the key things to know about containerized microservices.

Engineering data management is a key skill for data analysts who handle complex datasets across engineering systems. This field involves processes for managing, organizing, and optimizing data generated by engineering teams, such as IoT device data, operational metrics, and manufacturing details.

Took me years to finally get a Roomba (those robot vacuums from iRobot that have been around since 2002). I was pretty skeptical about how well it would clean and how smart and autonomous it really was. My wife was on board on the benefits from the start, but I needed some convincing. After plenty of back-and-forth (and my wife’s persistence), we went for it. Honestly, the benefits weren’t obvious to me from the start.

In today's data-driven landscape, data analysts rely on powerful database management tools to organize, query, and extract insights from vast datasets. With a multitude of options, choosing the right tool can significantly impact efficiency and performance. Tools like MySQL Workbench, SQL Server Management Studio, and Google BigQuery provide robust platforms for relational data management, while options such as MongoDB Compass cater to unstructured data needs.

Rack is the foundation for every popular Ruby web framework in existence. It standardizes an interface between a Ruby application and a web server. This mechanism allows us to pair any Rack-compliant web server (such as Puma, Unicorn, or Falcon) with any Rack-compliant web framework (like Rails, Sinatra, Roda, or Hanami). Separating the concerns like this is immensely powerful and provides a lot of flexibility. It does, however, also come with limitations.

A year in at Confluent, Product Manager Surabhi Singh has learned a lot about data streaming—and even more about herself. In this fast-paced environment, Surabhi is highly motivated and committed to her work strategically planning, coordinating, and delivering product improvements for customers whose business operations depend on Confluent Platform.