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.

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.

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.

Enjoy a love letter to Selenium from a veteran Saucer.

The world of quality assurance (QA) has changed, and there’s no turning back. Digital transformation is driving the need to produce high-quality software faster, while also keeping costs under control. The old idea of choosing between speed, cost-efficiency, or quality? That’s out the window. Today, you need all three to succeed, and they’ve got to work together.

Realtors have long been seen as the "connectors" between property buyers and sellers. However, in 2024, this role is increasingly being taken over by real estate websites like Zillow. So, what does this shift mean for real estate companies, startups, and agents? The demand for real estate apps is on the rise. People now prioritize quick and convenient ways to buy, sell, or rent properties, with about 95% of homebuyers beginning their search online.

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 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.