Here’s how to choose a relational database for your next project.
Regardless of the tech stack used, many developers have already used Redis or, at least, heard of it. Redis is specifically known for providing distributed caching mechanisms for cluster-based applications. While this is true, it’s not its only purpose. Redis is a powerful and versatile in-memory database. Powerful because it is incredibly super fast. Versatile because it can handle caching, database-like features, session management, real-time analytics, event streaming, etc.
In a previous post, C++ Microservices in Docker, we worked through the steps for creating a docker container that exposes a HydraExpress servlet container. We successfully deployed our HydraExpress server instance in Docker, however all that was available were the default example servlets. User application code wasn’t exposed. Let’s fix that and look at deploying custom C++ Servlet instances within the HydraExpress Docker container.
Companies these days are handling more data than ever: an average of 163 terabytes (163,000 gigabytes), according to a survey by IDG. Efficiently storing, processing and analyzing this data is essential in order to glean valuable insights and make informed business decisions. Yet the question remains: What is the best way to store enterprise data? For many use cases, the most appealing choice is a relational database.
What is multi-cloud data analytics and why are so many companies getting on board? Cloud computing itself is now a well-established best practice, but a multi-cloud strategy is nearly as common these days. While 94 percent of organizations are now using cloud computing, 84 percent are using a multi-cloud data strategy. Multi-cloud is an especially fruitful data strategy for companies pursuing data analytics.
