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Oracle vs VoltDB: What are the differences?
## Key Differences between Oracle and VoltDB
Oracle and VoltDB are both widely used database management systems, but they differ in several key aspects. Here are the main differences between the two:
1. **Architecture**: One major difference between Oracle and VoltDB is their architecture. Oracle follows a traditional disk-based architecture, where data is stored on disk and accessed when needed. In contrast, VoltDB uses an in-memory architecture, keeping all data in RAM for faster processing.
2. **Scalability**: Another significant difference is scalability. Oracle can scale vertically by adding more resources to a single server, while VoltDB is designed for horizontal scalability, allowing additional servers to be added to distribute the workload.
3. **Consistency**: Oracle utilizes a strong consistency model, ensuring that all data reads reflect the latest updates. VoltDB, on the other hand, implements an ACID-compliant distributed in-memory database, providing predictable and consistent performance even in distributed environments.
4. **Performance**: In terms of performance, VoltDB is known for its high throughput and low latency, ideal for real-time applications that require fast data processing. While Oracle also offers excellent performance, VoltDB's in-memory architecture gives it an edge in certain use cases.
5. **Ease of Use**: Oracle is a comprehensive database system with a wide range of features and functionalities, which can make it complex to manage. VoltDB, on the other hand, is known for its simplicity and ease of use, making it a preferred choice for developers looking for a streamlined solution.
6. **Use Cases**: Oracle is often used for traditional enterprise applications that require complex queries and support for various data types. VoltDB, on the other hand, is well-suited for real-time analytics, IoT applications, and other use cases that demand high-speed data processing.
In Summary, while Oracle is a robust and established database system suitable for a wide range of applications, VoltDB stands out for its in-memory architecture, scalability, and performance in real-time processing environments.
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.
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.
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.