The concept of insurance predates the printing press and the steam engine. So it's likely no surprise that the industry — as obviously adaptable as it is — often faces challenges related to the modernization of legacy systems. In the age of AI and APIs (application programming interfaces), streamlining operations and modernizing are paramount to success.

The retail industry is constantly evolving, and so is the way that businesses use BI tools. Retail business intelligence (BI) dashboards are now more important than ever, as they allow businesses to monitor their operations in a more comprehensive way. In this article, we explore the benefits of using BI dashboards in the retail industry, and discuss some of the ways in which they are transforming how it operates.

It's hard to overstate the importance of Cascading Style Sheets (CSS) for all websites. Since the first CSS standards were published in late 1996, we have come quite far regarding features and ecosystems. Several frameworks have appeared and proved popular, one of the most recent being Tailwind CSS. In this post, we'll first examine Tailwind's utility-first approach before diving into how to use it in a Ruby on Rails application.

AWS Fargate is a serverless compute engine that allows you to run containers without managing servers. With Fargate, you no longer have to provision clusters of virtual machines to run ECS containers: this is all done for you. Fargate has an Amazon ECS construct that can host an API. In this take, we will build a Fargate service using the AWS CDK, put the API in a docker image, and then host it inside Amazon ECS. The API will be a pizza API and we'll store the data in a DynamoDB table.

With the data management landscape continuously evolving, it has given rise to powerful platforms like Databricks and Snowflake, each offering distinct capabilities for organizations to manage and analyze their data efficiently. Our 5 key takeaways in the Databricks vs. Snowflake debate are: In this article, we will dive into a comprehensive comparison of Databricks and Snowflake and examine the data companies’ features, performance, scalability, and more.

Streaming ETL is a modern approach to extracting, transforming, and loading (ETL) that processes and moves data from source to destination in real-time. It relies on real-time data pipelines that process events as they occur. Events refer to various individual pieces of information within the data stream. Depending on the source and purpose of the data, an event could be a single user visit to a website, a new post on a social media platform, or a data point from a temperature sensor.