The need for real-time computing has resulted in the growth of many different in-memory computing (IMC) technologies. This includes caches, in-memory data grids, in-memory databases, streaming technologies and broader IMC platforms.  But what are the best technologies for each type of project? Learn about your options from one of the leading IMC veterans.

It used to be that the only way to improve application performance was to add a cache. But caches like Redis don't understand SQL. They require you to modify your applications with non-SQL coding and data models, and copy and synch data across two different models. They don't support ACID transactions very well. And they have their limits when it comes to scalability.

This eBook, Part 1 in the In-Memory Computing for Financial Services eBook Series, discusses how financial service firms are using in-memory computing platforms such as GridGain and Apache® Ignite™ to address the challenges of high-frequency trading, fraud prevention and real-time regulatory compliance.

This white paper covers the architecture, key capabilities, and features of GridGain®, as well as its key integrations for leading RDBMSs, Apache Spark™, Apache Cassandra™, MongoDB® and Apache Hadoop™. It describes how GridGain adds speed and unlimited horizontal scalability to existing or new OLTP or OLAP applications, HTAP applications, streaming analytics, and continuous learning use cases for machine or deep learning.

This in-depth feature comparison shows how the most current versions of GridGain Professional Edition, Enterprise Edition, Ultimate Edition and Redis Enterprise (and their respective open source projects where relevant) compare in 25 categories.