MongoDB Data Modeling and Schema Design
MongoDB has become increasingly popular as a leading NoSQL database. Its flexibility, scalability, and ease of use have made it a popular choice for many developers and businesses. MongoDB's document-based model allows for flexible storage of complex data and easy evolution of the schema.
As it turns out, data modeling is even more important for MongoDB than for relational databases, as there are no guardrails like the rules of normalization. The consequence is that the responsibility to ensure consistency, integrity, and quality shifts elsewhere.
Learn how to properly design your data model with MongoDB to avoid problems later.
Our role with this book is to make you aware of all of the possibilities, cover the pros and cons, and share use cases. This should inspire readers as they design their schemas. The book includes a recipe for a proven approach to data modeling. It is a toolbox in which we describe no less than 16 different schema design patterns for MongoDB documents.
MongoDB design patterns are reusable solutions for many of the commonly occurring use cases encountered when designing applications that leverage persistence in MongoDB.
Read MongoDB Data Modeling and Schema Design if you are a data architect or modeler who needs to expand your modeling skills to include MongoDB, or if you are a database administrator or developer who knows MongoDB but needs to expand your schema design skills.
Most of the principles in this book are applicable to other NoSQL document databases, such as Couchbase and Amazon DocumentDB.
Purchase your copy of the book on:
Neo4j Data Modeling
Neo4j is a popular graph database that has gained popularity in recent years. Its strength lies in its ability to store and query highly connected data, making it a popular choice for applications such as social networks, recommendation engines, and fraud detection. Neo4j's unique property graph model enables developers to store data as nodes and relationships, providing a rich and expressive way to model complex domains.
Read Neo4j Data Modeling if you are a data architect or modeler who needs to expand your modeling skills to include Neo4j, or if you are a database administrator or developer who knows Neo4j but needs to expand your schema design skills.
Purchase your copy of the book on:
Data Modeling and Schema Design for Oracle 23c JSON-Relational Duality Views
With Oracle 23c new duality views, documents are materialized, generated on demand, not stored as such. Duality views give your data both a conceptual and an operational duality: it’s organized both relationally and hierarchically. You can base different duality views on data stored in one or more relational tables, providing different JSON hierarchies over the same, shared data. This means that applications can access (create, query, modify) the same data as a set of JSON documents or as a set of related tables and columns, and both approaches can be employed at the same time.
In this book you will learn how to:
- Not be forced into making compromises between normalization and NoSQL
- Eliminate data duplication and the risk of inconsistencies when working with JSON documents
- Design the optimal schema for duality views and avoid object-relational impedance mismatch for your developers
Read Oracle 23c JSON-Relational Duality Views Data Modeling and Schema Design if you are a data architect or modeler who needs to expand your modeling skills to include Oracle 23c Duality Views, or if you are a database administrator or developer who knows Oracle but needs to expand your schema design skills.
Pre-order your copy of the book on:
Data Modeling and Schema Design for Elasticsearch
Elasticsearch, although primarily known as a search and analytics engine, can also be considered as a NoSQL database. It is designed to store and retrieve large volumes of structured and unstructured data in a distributed and scalable manner. Elasticsearch stores data in a schema-less JSON format, allowing for flexible and dynamic data modeling. It provides powerful indexing and querying capabilities, allowing you to search and retrieve data based on various criteria and perform aggregations for analytics purposes. With its distributed architecture and built-in replication, Elasticsearch ensures high availability and fault tolerance. It's a popular choice for applications that require fast and efficient data retrieval, real-time analytics, and horizontal scalability.
Read Elasticsearch Data Modeling and Schema Design if you are a data architect or modeler who needs to expand your modeling skills to include Elasticsearch, or if you are a database administrator or developer who knows Elasticsearch but needs to expand your schema design skills.