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This week The apache Ignite book becomes one of the top books of leanpub.

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 ...

Ignite Baseline Topology or BLT represents a set of server nodes in the cluster that persists data on disk. Where, N1-2 and N5 server nodes are the member of the Ignite clusters with native persistence which enable data to persist on disk. N3-4 and N6 server nodes are the member of the Ignite cluster but not a part of the baseline topology. The nodes from the baseline topology are a regular server node, that store's data in memory and on the disk, and also participates in computing tasks. Ignite clusters can have different nodes that are not a part of the baseline topology such as: Server nodes that are not used Ignite native persistence to persist data on disk. Usually, they store data in memory or persists data to a 3rd party database or NoSQL. In the above equitation, node N3 or N4 might be one of them. Client nodes that are not stored shared data. To better understand the baseline topology concept, let’s start at the beginning and try to understand its goal and what ...

Apache Ignite is widely used around the world and is growing all the time. Companies like Barclays, Misys, Sberbank (3r largest bank in Europe), ING, JacTravel all use Ignite to power pieces of their architecture that are critical to the day-to-day operations of those organizations. Moreover, the vendor like TIBCO uses core caching data-grid module of Apache Ignite with advanced indexing and SQL capability for their Master Data Management platform. However there are a few others alternatives to Apache Ignite from other vendors such as HazelCast, Oracle, Ehcache, GemFire, etc. The main difference of Apache Ignite from the others is the number of functionalities and simplicity of use. Apache Ignite provides a variety of functionalities, which you can use for different use cases. The key differences between the Apache Ignite , Hazelcast , and Apache Cassandra are as follows: Feature Apache Ignite Hazelcast Apache Cassandra Data model Key-value ...

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...