With the cost of system memory dropping 30% every 12 months, in-memory computing has become the first choice for a variety of workloads across all industries. In-memory computing can provide a lower TCO for data processing systems while providing an unparalleled performance advantage.In-memory computing technologies take many forms, ranging from in-memory data caches on a single server to in-memory databases (IMDBs), in-memory data grids (IMDGs), and comprehensive in-memory computing platforms (IMCPs). High performance in-memory computing technologies can even allow real-time analytics to run on operational datasets, enabling the implementation of hybrid transactional/analytical processing (HTAP) systems that can provide significant cost and complexity savings.The GridGain in-memory computing platform provides a way to simplify the HTAP architecture for IoT databases and analytics. It addresses the needs of both transactional and analytical processing and also provides persistency and event processing — all in a high-speed, linearly scalable platform. And GridGain is just one core technology with one skill set to learn.This white paper provides an overview of in-memory computing technology with a focus on in-memory data grids. It discusses the advantages and uses of in-memory data grids and introduces the GridGain In-Memory Data Fabric. Finally, it presents a deep dive on the capabilities of the GridGain solution.