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A few sample chapters of our new title The Apache Ignite book has been published. The updated full table of contents also available at the book landing page . This is an agile-published book and the first portion of the book will be published soon. We want this book to be the perfect guide for the Ignite users. So, any suggestions, comments, ideas are welcome.

This is the table of contents of the Apache Ignite book that we are planning to publish end of this year 2018. Table of contents: Chapter 1. Introduction Chapter 2. Getting started with Apache Ignite Installing and setting up Apache Ignite Building from source code Run multiple instances of Apache Ignite in a single host Running Apache Ignite from Docker Using Apache Ignite SQLLINE command tool Meet with Apache Ignite SQL engine: H2 database Using a universal SQL client IDE to working with Apache Ignite Apache Ignite thin client First Java application Using REST API for manipulating the Apache Ignite caches Configure a multimode cluster in different hosts Summary What's Next Chapter 3.  Apache Ignite use cases Caching for fast data access High volume transaction processing HTAP Fast data processing Lambda architecture Resilient web acceleration Microservices in distributed fashion Cache as a service Big Data accelerations In-memory machine learn...

After a year, we have decided to write down another book related to the Apache Ignite distributed database. Within a year, Apache Ignite team redesigned the memory architecture and released a few new versions which cover new features such as Native persistence, baseline topology. Apache Ignite also optimized the performance of the SQL, added new features like Alter tables to DDL and also introduced SQLLINE command line tool for SQL based interaction. From this given release Apache Ignite team revisited the definition and purpose of the project. By their words, the definition "in-memory data fabrics/grids" limits its capabilities, rather than the distributed database, caching, and processing platform. So, in this book, we are going to cover the following topics: Apache Ignite architecture in details to build right solutions to given business problems. Use cases of using in-memory databases How Apache Ignite SQL works and how you can optimize the SQL engine to get better pe...

Portions of this article were taken from the book  The Apache Ignite book . If it got you interested, check out the rest of the book for more helpful information. Spark Data frame is included in new book. Apache Ignite offers several ways to improve a Spark job's performance: Ignite RDD, which represents an Ignite cache as a Spark RDD abstraction, and Ignite IGFS, an in-memory file system that can be transparently plugged into Spark deployments. Ignite RDD allows easily sharing states in-memory between different Spark jobs or applications. With Ignite in-memory shares RDDs, any Spark job can put some data into an Ignite cache that other Spark jobs can access later. Ignite RDD is implemented as a view over the Ignite distributed cache, which can be deployed either within the Spark job execution process or on a Spark worker. Before we move on to more advanced topics, let's have a look at the history of Spark and what kinds of problems can be solved by Ignite RDDs. ...

A new title  The Apache Ignite book  has been released including Ignite 2.6 and above. Read the full book review by Sadruddin Md .

Portions of this article were taken from the book  The Apache Ignite book . If it got you interested, check out the rest of the book for more helpful information. In-memory approaches can achieve blazing speed by putting the working set of the data into the system memory. When all data is kept in memory, the need to deal with issues arising from the use of traditional spinning disks disappears. This means, for instance, there is no need to maintain additional cache copies of data and manage synchronization between them. But there is also a downside to this approach because the data is in memory only, it will not survive if the whole cluster gets terminated. Therefore, this types of data stores are not considered persistence at all. In this blog post, I will do an effort to explore the Apache Ignite new native persistence feature and provide a clear, understandable picture how the Apache Ignite native persistence works.  In most cases, you can’t (should not) store the wh...

See more details on The Apache Ignite Book . Spring Data  provides a unified and easy way to access the different kinds of persistence store, both relational database systems, and NoSQL data stores. It is on top of JPA, adding another layer of abstraction and defining a standard-based design to support persistence Layer in a Spring context. Apache Ignite  IgniteRepository  implements Spring Data CrudRepository interface and extends basic capabilities of the  CrudRepository , which in turns supports: Basic CRUD operations on a repository for a specific type. Access to the Apache Ignite SQL grid via Spring Data API. With Spring Data's repositories, you only need to write an interface with finder methods to query the objects. All the CRUD method for manipulating the objects will be delivered automatically. As an example: @RepositoryConfig(cacheName = "DogCache") public interface DogRepository extends IgniteRepository<Dog, Long> { List<Do...