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Datomic vs Oracle: What are the differences?

Introduction: When comparing Datomic and Oracle, there are key differences that set these two technologies apart in the realm of database management. Below, we outline the specific distinctions between Datomic and Oracle in a concise manner.

  1. Data Model: Datomic utilizes an immutable, append-only data model, where historical data is preserved and can be queried at any point in time. On the other hand, Oracle employs a more traditional mutable data model, where data can be updated and overwritten in real-time.

  2. Scalability: Datomic is horizontally scalable, allowing for seamless distribution of data across multiple nodes. In contrast, Oracle traditionally relies on vertical scalability, where resources are added to a single node to handle increased data loads.

  3. Architecture: Datomic follows a client-server architecture, where clients interact with a centralized server for data access. Oracle, on the other hand, typically employs a monolithic architecture, where all components reside on a single server.

  4. Query Language: Datomic uses Datalog, a declarative query language that supports rich querying capabilities and is based on logical predicates. Oracle utilizes SQL (Structured Query Language), a more common and widely-used language for querying relational databases.

  5. Consistency Model: Datomic ensures strong consistency in a distributed system through its use of transactions and timestamp-based isolation. Oracle offers both strong and eventual consistency models, depending on the configuration and use case.

  6. Licensing Model: Datomic follows a unique licensing model based on peer count, where the cost is determined by the number of peers (instances) connected to the database. Oracle, on the other hand, offers different licensing options based on factors like the number of users, cores, or sockets.

In Summary, Datomic and Oracle differ in their data models, scalability options, architecture, query languages, consistency models, and licensing approaches, making them distinct choices for database management solutions.

Decisions about Datomic and Oracle
Daniel Moya
Data Engineer at Dimensigon · | 4 upvotes · 419.2K views

We have chosen Tibero over Oracle because we want to offer a PL/SQL-as-a-Service that the users can deploy in any Cloud without concerns from our website at some standard cost. With Oracle Database, developers would have to worry about what they implement and the related costs of each feature but the licensing model from Tibero is just 1 price and we have all features included, so we don't have to worry and developers using our SQLaaS neither. PostgreSQL would be open source. We have chosen Tibero over Oracle because we want to offer a PL/SQL that you can deploy in any Cloud without concerns. PostgreSQL would be the open source option but we need to offer an SQLaaS with encryption and more enterprise features in the background and best value option we have found, it was Tibero Database for PL/SQL-based applications.

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We wanted a JSON datastore that could save the state of our bioinformatics visualizations without destructive normalization. As a leading NoSQL data storage technology, MongoDB has been a perfect fit for our needs. Plus it's open source, and has an enterprise SLA scale-out path, with support of hosted solutions like Atlas. Mongo has been an absolute champ. So much so that SQL and Oracle have begun shipping JSON column types as a new feature for their databases. And when Fast Healthcare Interoperability Resources (FHIR) announced support for JSON, we basically had our FHIR datalake technology.

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In the field of bioinformatics, we regularly work with hierarchical and unstructured document data. Unstructured text data from PDFs, image data from radiographs, phylogenetic trees and cladograms, network graphs, streaming ECG data... none of it fits into a traditional SQL database particularly well. As such, we prefer to use document oriented databases.

MongoDB is probably the oldest component in our stack besides Javascript, having been in it for over 5 years. At the time, we were looking for a technology that could simply cache our data visualization state (stored in JSON) in a database as-is without any destructive normalization. MongoDB was the perfect tool; and has been exceeding expectations ever since.

Trivia fact: some of the earliest electronic medical records (EMRs) used a document oriented database called MUMPS as early as the 1960s, prior to the invention of SQL. MUMPS is still in use today in systems like Epic and VistA, and stores upwards of 40% of all medical records at hospitals. So, we saw MongoDB as something as a 21st century version of the MUMPS database.

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Pros of Datomic
Pros of Oracle
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    • 44
      Reliable
    • 33
      Enterprise
    • 15
      High Availability
    • 5
      Expensive
    • 5
      Hard to maintain
    • 4
      Maintainable
    • 4
      Hard to use
    • 3
      High complexity

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    Cons of Datomic
    Cons of Oracle
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      • 14
        Expensive

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      What is Datomic?

      Build flexible, distributed systems that can leverage the entire history of your critical data, not just the most current state. Build them on your existing infrastructure or jump straight to the cloud.

      What is Oracle?

      Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database.

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      What companies use Datomic?
      What companies use Oracle?
      See which teams inside your own company are using Datomic or Oracle.
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      What tools integrate with Datomic?
      What tools integrate with Oracle?
        No integrations found

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        What are some alternatives to Datomic and Oracle?
        MongoDB
        MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
        Cassandra
        Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
        Kafka
        Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
        Neo4j
        Neo4j stores data in nodes connected by directed, typed relationships with properties on both, also known as a Property Graph. It is a high performance graph store with all the features expected of a mature and robust database, like a friendly query language and ACID transactions.
        GraphQL
        GraphQL is a data query language and runtime designed and used at Facebook to request and deliver data to mobile and web apps since 2012.
        See all alternatives