Gain insights into integrating Spring Data with databases like JDBC, JPA, MongoDB, Redis, Neo4j, Cassandra, and Elasticsearch. Learn to create applications connecting seamlessly to diverse data sources.

spring_data.tar.gz

spring_data_chapter1

spring_data_jdbc_chapter3

spring_data_jpa_chapter4

spring_data_mongodb_chapter5

spring_data_redis_chapter6

spring_data_couchbase_chapter7

spring_data_elasticsearch_chapter8

spring_data_neo4j_chapter9

spring_data_cassandra_chapter10

spring_data_geode_chapter11

spring_data_solr_chapter12

spring_data_chapter2

spring_data_mongodb_chapter5-transaction

This course will teach you how to seamlessly integrate Spring Data with various data sources, including relational and nonrelational databases, map-reduce frameworks, and cloud-based data services. 

You’ll start this course by exploring Spring Data JDBC and Spring Data JPA for efficient access to relational databases. Next, you’ll dive into NoSQL databases with Spring Data MongoDB and Spring Data Redis, venture into graph databases with Spring Data Neo4j, and explore the world of distributed databases with Spring Data for Apache Cassandra. Lastly, you’ll discover the power of Spring Data Elasticsearch. 

By the end of this course, you’ll emerge equipped with the knowledge and skills to leverage Spring Data effectively in diverse data-driven scenarios. You’ll be able to create applications seamlessly integrated with various data sources. This proficiency in Spring Data will enable you to design, build, and maintain applications that interact effortlessly with databases and data services.

Spring Data: Bridging Multiple Databases

Extend the existing library management system to include a book recommendation system. It should give book recommendations based on the genres the users provide.

In this challenge, complete the following tasks:

1. Create the `Genre` class in the `com.smartdiscover.model` package to store various genres available in our library management system.

1. Add properties like `id` and `genreText` to the `Genre` class and add annotations to transform the POJO into a Neo4j graph node. Also, create the `GenreText` enum in the same class with a few values like `SCIENCE_FICTION`, `FICTION`, and `THRILLER`.

1. Similarly, create the `BookRating` class in the `com.smartdiscover.model` package to store a list of a book's ratings.

1. Add properties like `id` and `rating` to the `BookRating` class and add annotations to transform the POJO into a Neo4j graph node.

1. Update the `Book` class in the `com.smartdiscover.model` package to store a  `Genre` list and a `BookRating` list. Also, create a method named `getAverageRating` in the `Book` class to return the average of all of a book's ratings.

1. Create the `GenreRepository` interface in `com.smartdiscover.repository` as a repository layer for the `Genre` POJO class and an abstract method to find the `Genre` object matching the `genreText` property.

1. Create the `BookRatingRepository` interface in the `com.smartdiscover.repository` package for the `BookRating` POJO class.

1. Add a method in the `BookRepository` to search all the `Book` objects with the matching `id` of the `Genre` object.

1. Inject the new `GenreReposotory` and `BookRatingRepository` in the `SpringDataNeo4jApplication` class and create the `recommendBook` method accepting the genre name and return a list of book names as a recommendation.

Challenge yourself to implement a book recommendation system using Spring Data Neo4j.

Introduction to Spring Data

Spring Data in Detail

Spring Data JDBC

Spring Data JPA

Spring Data MongoDB

Spring Data Redis

Spring Data Couchbase

Spring Data Neo4j

Spring Data for Apache Cassandra

Spring Data Elasticsearch

Bank Management System

Wrapping Up

Challenge: Book Recommendation System

Implement a book recommendation system