Chapter 14: Integrating Clojure with Datomic

In this section

• 14.1 Introduction to Datomic's Architecture and Philosophy
  • Datomic's Immutable Database Model: A Deep Dive into Immutability and Time Travel
    Explore Datomic's innovative immutable database model, its core components, and how it supports time travel and separation of reads and writes for scalable data solutions.
  • Benefits of Using Datomic: Scalability, Rich Querying, and Data Integrity
    Explore the benefits of using Datomic, including scalability, rich querying with Datalog, and data integrity through ACID transactions and schema enforcement.
• 14.2 Working with Datomic's Immutable Database Model
  • Setting Up Datomic: A Comprehensive Guide for Java Developers
    Learn how to set up Datomic for scalable data solutions using Clojure, including installation, storage backend selection, and transactor configuration.
  • Connecting Clojure Applications to Datomic: A Comprehensive Guide
    Explore how to connect Clojure applications to Datomic, including setting up dependencies, establishing connections, and managing databases effectively.
  • Defining Schemas in Clojure and NoSQL: Mastering Data Modeling with Datomic
    Explore the intricacies of defining schemas in Clojure using Datomic, focusing on attributes, entity types, and referential integrity for scalable NoSQL solutions.
• 14.3 Writing Queries with Datalog
  • Datalog Query Language: A Comprehensive Introduction for Clojure and NoSQL
    Explore the fundamentals of the Datalog query language, its syntax, and how it integrates with Clojure for querying NoSQL databases like Datomic.
  • Advanced Query Techniques in Clojure for NoSQL Databases
    Explore advanced query techniques in Clojure for NoSQL databases, including pattern matching, joins, aggregation functions, recursive queries, and full-text search.
• 14.4 Temporal Data and Point-in-Time Queries
  • Time Travel Queries in Datomic: Exploring As-Of Queries and History API
    Explore the power of time travel queries in Datomic using As-Of Queries and the History API to analyze data as it was at any point in time. Learn how to implement these features in Clojure for scalable data solutions.
  • Bitemporal Modeling in Clojure and NoSQL: A Comprehensive Guide
    Explore the intricacies of bitemporal modeling in Clojure and NoSQL databases, focusing on transaction and valid time, temporal queries, and practical implementation strategies.
• 14.5 Scaling Datomic for Enterprise Applications
  • Read Scalability with Peers and Peer Servers in Datomic
    Explore how to enhance read scalability in Datomic using peers and peer servers, including caching strategies and performance optimization.
  • Scalability Considerations for Datomic in Clojure Applications
    Explore scalability considerations for Datomic in Clojure applications, focusing on the single transactor model, transaction optimization, and monitoring strategies.
• 14.6 Case Study: Knowledge Graphs with Datomic
  • Modeling a Knowledge Graph with Clojure and Datomic
    Explore how to model a knowledge graph using Clojure and Datomic, focusing on entities, relationships, schema design, and practical implementation.
  • Querying Complex Relationships in NoSQL with Clojure
    Explore how to query complex relationships in NoSQL databases using Clojure, focusing on recursive queries, pattern matching, and leveraging Datalog rules for efficient data retrieval.
  • Leveraging Datomic's Features for Temporal Analysis and Scalability
    Explore how to leverage Datomic's unique features for temporal analysis, integration with analytics tools, and optimizing scalability and performance in Clojure applications.