Struggling to choose between H2 Database Engine and Datomic? Both products offer unique advantages, making it a tough decision.
H2 Database Engine is a Development solution with tags like sql, jdbc, java, opensource, relational, embedded, clientserver.
It boasts features such as Embedded and server modes, Pure Java implementation, Very small footprint, SQL and JDBC support, Disk-based or in-memory databases, Browser-based Console application and pros including Lightweight and fast, Easy to embed in applications, Good for prototyping and testing, Developer-friendly, Written in Java - works anywhere Java works.
On the other hand, Datomic is a Development product tagged with distributed, datalog, acid, temporal, flexible-data-modeling, scalable.
Its standout features include Distributed database, Uses Datalog query language, ACID transactions, Temporal/historical database capabilities, Flexible schema design, Scalable across multiple servers, and it shines with pros like Handles large datasets and scales horizontally, Flexible schema allows for evolving data models, Query performance optimized for reads, Built-in caching improves read speeds, ACID transactions ensure data consistency, Temporal querying enables data auditing.
To help you make an informed decision, we've compiled a comprehensive comparison of these two products, delving into their features, pros, cons, pricing, and more. Get ready to explore the nuances that set them apart and determine which one is the perfect fit for your requirements.
H2 is an open-source relational database management system written in Java. It can be embedded in Java applications or run in client-server mode. H2 supports SQL and JDBC APIs and has a small footprint, making it well-suited for testing, prototyping, and small applications.
Datomic is a distributed database designed to enable scalable, flexible and intelligent applications. It uses Datalog and transaction processing to provide ACID transactions, temporal database capabilities and flexible data modeling. Datomic is well suited for applications that need to manage large data sets across multiple servers.