This book is addressed to anyone interested in learning in-memory computing and distributed database. It is useful for developers and architects new to Apache Ignite as well as those who already use any other in-memory databases and want to expand their knowledge.

The source code of the book is updated to Ignite version 2.14.0 and is available at GitHub

We upload a PDF file that has color images of the diagrams used in this book. You can download it here https://bit.ly/36Mw0y9

What you will learn (please see the table of contents below for more details):

1. Apache Ignite architecture in depth such as data distributing technics (DHT), Rendezvous hashing, durable memory architecture, various cluster topologies, Ignite native persistence, Baseline topology, and much more.
2. Apache Ignite proven use cases as a memory-centric distributed database, caching, and computing platforms.
3. Getting started with Apache Ignite by using different tools and technics.
4. Caching strategies by examples and how to use Apache Ignite for improving application performance including Hibernate L2 cache, MyBatis, Memoization, and Web session clustering.
5. Using Spring Data with Apache Ignite for developing high-performance web applications.
6. Ignite query (SQL, API, Text, and Scan queries) capabilities in depth.
7. Using Spark RDD and Data frames for improving performance on processing fast data.
8. Developing and executing distributed computations in a parallel fashion to gain high performance, low latency, and linear scalability. 
9. Developing distributed Microservices in fault-tolerant fashion.
10. Processing events & streaming data for IoT projects, integrate Apache Ignite with other frameworks like Kafka, Storm, Camel, etc.
11. Real-time data Replication between Ignite clusters through Kafka.
12. Configuring, management and monitoring Ignite cluster with built-in and 3^rd party tools.

For every topic, a complete application is delivered, which helps the audience to a quick start with the topic. Each chapter focuses on the complete implementation of a real-world scenario, the commonly occurring challenges in each scenario have also discussed, along with tips and tricks and best practices on how to overcome them.

Readership:

The target audiences of this book will be an IT architect, team leader, or programmer with minimum programming knowledge. No excessive knowledge is required, though it would be good to be familiar with Java, Spring framework, and tools like Maven. The book is also useful for any reader, who already familiar with Oracle Coherence, Hazelcast, Infinispan, or Memcached.

If you are not sure if this book is for you, I suggest you read the sample chapter of the book. The sample chapter is available in different formats including HTML. Anyway, I encourage you to try it out, and if you don't like the book, you can always ask a 100% refund within 45 days.

The entire source code of the book is available at GitHub

We need your feedback!

We would like to hear what you think, what you like or dislike the content of the book. Your feedback will help us to write a better book and help others to clear all the concepts. To submit your feedback, please use the feedback link below.

This book is a practical guide for anyone interested in diving into the world of Generative AI development, regardless of their prior programming experience.

Here's what you can expect:

1. Clear and concise explanations: The book breaks down complex AI concepts into easily understandable steps, making it accessible to beginners.
2. Step-by-step instructions: Each chapter guides you through building a specific AI application, from setting up your environment to deploying your final product.
3. Real-world examples: You'll learn by applying AI techniques to solve practical problems, gaining valuable hands-on experience.
4. Popular tools and libraries: The book focuses on widely used tools and libraries like Langchain, Vanna, TensorFlow, and PyTorch equipping you with in-demand skills.
5. Project-based learning: You'll work on engaging projects that range from simple image recognition to more advanced natural language processing tasks.

By the end of this book, you'll be able to:

1. Understand the fundamentals of Generative AI and Large Language Models (LLMs).
2. Enrich RAG (Retrieval Augmented Generation) LLM models with your own datasets, such as PDFs and documents.
3. Interact between LLM models and SQL databases.
4. Build and train your own AI models.
5. Utilize AI agents to perform tasks without human intervention.
6. Deploy your AI applications in real-world scenarios.
7. Gain confidence in your ability to develop innovative AI solutions.

Whether you're a student, a professional looking to upskill, or simply someone curious about AI, this book provides a comprehensive and practical roadmap to becoming an AI developer.