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Altibase vs Oracle: What are the differences?
Key Differences between Altibase and Oracle
Data Storage Architecture: Altibase uses a hybrid architecture combining in-memory and on-disk storage, allowing for high performance and scalability. In contrast, Oracle primarily relies on disk-based storage, with the option to use in-memory features, giving Altibase an advantage in terms of speed and efficiency.
Data Replication Capabilities: Altibase offers active-active replication, allowing for simultaneous read and write operations on multiple nodes, ensuring high availability and reliability. On the other hand, Oracle typically provides active-passive replication, leading to potential performance limitations in comparison to Altibase.
Cost Structure: Altibase adopts a subscription-based pricing model, which can be more cost-effective for organizations looking for flexibility and scalability in their database management. Oracle, on the other hand, often entails significant upfront licensing fees, making it expensive for businesses with budget constraints or fluctuating data requirements.
Support for Advanced Analytics: Altibase is well-suited for complex analytical workloads, offering in-database analytics and machine learning capabilities. In contrast, Oracle also provides advanced analytics features but may require additional licensing or integration with other tools, making Altibase a more integrated solution for organizations focused on analytics.
Flexibility in Deployment Options: Altibase supports both on-premises and cloud deployment options, giving organizations the flexibility to choose the environment that best meets their needs. Oracle predominantly focuses on on-premises deployment, with cloud options that may require additional considerations or configurations, giving Altibase an edge in terms of adaptability.
In Summary, Altibase and Oracle differ in their data storage architecture, data replication capabilities, cost structure, support for advanced analytics, and flexibility in deployment options.
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.
Pros of Altibase
Pros of Oracle
- Reliable44
- Enterprise33
- High Availability15
- Hard to maintain5
- Expensive5
- Maintainable4
- Hard to use4
- High complexity3
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Cons of Altibase
Cons of Oracle
- Expensive14
