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Recently we made a massive update on our lean publishing book The Apache Ignite Book . The book still is in progress. However, for the developers who intended to a quick start with Apache Ignite, we restructured the Sample chapter such a way that can be used as a  quick start guide . We included the entire chapter 2 Getting started with Apache Ignite and most of the part of chapter 4 Architecture deep dive . We believe the best way to learn something new is to jump right in and do a simple example to play around it. Whenever you had a decent overview of what you could do with this technology, you could always find more details later. The document is well structured and guides you from installing and running Ignite to write an advanced application to read and write data from/to the Apache Ignite database. The Apache Ignite quick start guide covers the following topics: Chapter: Getting started with Apache Ignite Installing and setting up Apache Ignite. Running multiple ...

Apache Ignite, from version 1.6 on, provides a new way to do data processing based on Kafka Connect. Kafka Connect, a new feature introduced in Apache Kafka 0.9, enables scalable and reliable streaming data between Apache Kafka and other data systems. It made it easy to add new systems to your scalable and secure stream data pipelines in-memory. In this article, we are going to look at how to set up and configure the Ignite Source connector to perform data replication between Ignite clusters. Apache Ignite, out-of-the-box, provides the  Ignite-Kafka   module with three different solutions (API) to achieve a robust data processing pipeline for streaming data from/to Kafka topics into Apache Ignite. In a nutshell, Apache   IgniteSourceConnector   is used to subscribe to Ignite cache events and stream them to Kafka topics. In other words, it can be used to export data (changed datasets) from an Ignite cache and write the contents into a Kafka topic using only a configur...

Apache Ignite  is an open source memory-centric distributed database, caching and comput- ing platform. From the beginning, it was designed as an in-memory data grid for developing a high-performance software system. So, it’s core architecture design is slightly different from the traditional NoSQL databases, which can simplify building modern applications with a flexible data model and simpler high availability, high scalability.     Moreover, to understand how to design application with any databases or framework properly, you must understand the architecture of the database or framework itself. By getting a better idea of the system, you can solve different problems in your enterprise architecture landscape, can select a comprehensive database or framework that is appropriate for your application and get the maximum benefits from the system. In this article we are going to explore the Apache Ignite SQL engine.      Under the hood...

We are happy to introducing our new title " The Apache Ignite book ". The first portion of the book is being published a few days before by leanpub publication. Note that, the entire book is an agile published book, which means that the authors are releasing it as they write it and will continue to update the book as the underlying technology evolves over time. The sample chapter of the book is available in different formats including HTML . What you will learn: Apache Ignite architecture in depth such as data distributing technics (DHT), Rendezvous hashing, memory architecture, various topology and caching strategies. Apache Ignite use cases as a memory-centric distributed database, caching and computing platforms. Caching strategies and how to use Apache Ignite to improve the performance of the application. Using JPA with Apache Ignite for developing high-performance web applications . How to accelerates the performance of your existing Hadoop ecosystem withou...

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.

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. ...