Key features

• •You have a high volume of ACID transactions in your system.
• •You have database bottleneck in your application and want to solve the problem.
• •You want to develop and deploy Microservices in a distributed fashion.
• •You have an existing Hadoop ecosystem (OLAP) and want to improve the performance of map/reduce jobs without making any changes in your existing map/reduce jobs.
• •You want to share Spark RDD directly in-memory (without storing the state into the disk)
• •You are planning to process continuous never-ending streams and complex events of data.
• •You want to use distributed computations in parallel fashion to gain high performance.
• •In-memory data fabrics use-cases and how it can help you to develop near real-time applications.
• •In-memory data fabrics detail architecture.
• •Caching strategies and how to use In-memory caching to improve the performance of the applications.
• •SQL grid for in-memory caches.
• •How to accelerates the performance of your existing Hadoop ecosystem without changing any code.
• •Sharing Spark RDD states between different Spark applications for improving performance.
• •Processing events & streaming data, integrate Apache Ignite with other frameworks like Storm, Camel, etc.
• •Using distributed computing for building low-latency software.
• •Developing distributed Microservices in fault-tolerant fashion.