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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
This book covers a verity of topics, including in-memory data grid, highly available service grid, streaming (event processing for IoT and fast data) and in-memory computing use cases from high-performance computing to get performance gains. The book will be particularly useful for those, who have the following use cases: 1) You have a high volume of ACID transactions in your system. 2) You have database bottleneck in your application and want to solve the problem. 3) You want to develop and deploy Microservices in a distributed fashion. 4) 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. 5) You want to share Spark RDD directly in-memory (without storing the state into the disk) 7) You are planning to process continuous never-ending streams and complex events of data. 8) You want to use distributed computations in parallel fashion to gain high performance.
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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
This book covers a verity of topics, including in-memory data grid, highly available service grid, streaming (event processing for IoT and fast data) and in-memory computing use cases from high-performance computing to get performance gains. The book will be particularly useful for those, who have the following use cases: 1) You have a high volume of ACID transactions in your system. 2) You have database bottleneck in your application and want to solve the problem. 3) You want to develop and deploy Microservices in a distributed fashion. 4) 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. 5) You want to share Spark RDD directly in-memory (without storing the state into the disk) 7) You are planning to process continuous never-ending streams and complex events of data. 8) You want to use distributed computations in parallel fashion to gain high performance.