Chapter 16: Emerging Trends and Technologies

In this section

• 16.1 New Developments in NoSQL Databases
  • NoSQL and SQL Convergence: Bridging Scalability and Consistency
    Explore the convergence of NoSQL and SQL technologies, focusing on NewSQL databases like CockroachDB and Google Spanner, and their role in providing scalable, consistent data solutions.
  • Multi-Model Databases: Flexibility and Power in a Single System
    Explore the world of multi-model databases, their advantages, considerations, and practical use with Clojure for scalable data solutions.
• 16.2 Incorporating Machine Learning and AI with NoSQL Data
  • Preparing NoSQL Data for Machine Learning: A Comprehensive Guide for Clojure and NoSQL
    Explore the intricate process of preparing NoSQL data for machine learning applications using Clojure. Learn about ETL processes, data cleaning, and preprocessing techniques to transform unstructured data into ML-ready formats.
  • Building Machine Learning Models in Clojure: A Comprehensive Guide
    Explore how to build and deploy machine learning models in Clojure using libraries like DeepLearning4J and SMILE. Learn about model training, evaluation, and integration with applications.
• 16.3 GraphQL and Clojure for API Development
  • Understanding GraphQL for Scalable Data Solutions
    Explore the core concepts, benefits, and implementation strategies of GraphQL in the context of Clojure and NoSQL databases, tailored for Java developers.
  • Implementing GraphQL Servers with Lacinia in Clojure
    Learn how to implement GraphQL servers in Clojure using Lacinia, a pure Clojure implementation of the GraphQL specification. Explore schema definition, resolver functions, and integration with NoSQL databases.
  • Integrating with NoSQL Databases: Efficient Data Retrieval and Handling Relationships
    Explore advanced techniques for integrating NoSQL databases with Clojure, focusing on efficient data retrieval and handling complex relationships using GraphQL and Clojure resolvers.
• 16.4 The Role of Functional Programming in Big Data
  • Advantages of Functional Programming: Enhancing Clojure and NoSQL Solutions
    Explore the advantages of functional programming in Clojure for scalable NoSQL data solutions, focusing on immutability, first-class functions, and more.
  • Clojure in Data Processing Ecosystems: Harnessing Big Data with Functional Elegance
    Explore how Clojure integrates with data processing ecosystems, leveraging Java interoperability, Apache Storm, and Onyx for scalable, real-time data solutions.
• 16.5 Preparing for the Future: Skills and Knowledge Areas
  • Continuous Learning and Adaptation: Mastering Clojure and NoSQL
    Explore strategies for continuous learning and adaptation in Clojure and NoSQL, including staying updated with industry trends, contributing to open source, and expanding your skill set.
  • Embracing New Technologies in Clojure and NoSQL
    Explore how Java developers can embrace new technologies in Clojure and NoSQL to design scalable data solutions, focusing on experimentation, practical application, and networking.