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IBM Watson vs Oracle: What are the differences?
Focus on AI Capabilities: IBM Watson is known for its strong focus on artificial intelligence, particularly in areas such as natural language processing, machine learning, and conversational interfaces. On the other hand, Oracle's AI capabilities are often integrated into its broader suite of enterprise solutions, with a focus on enhancing business operations and decision-making.
Cloud Offerings: IBM Watson primarily offers cloud-based AI services and solutions, including cognitive computing capabilities, while Oracle provides a wide range of cloud services beyond AI, such as database management, enterprise applications, and infrastructure solutions. IBM Watson is more specialized in AI-centric cloud offerings, whereas Oracle offers a more comprehensive cloud portfolio.
Industry Focus: IBM Watson has established a strong presence in industries such as healthcare, finance, and retail, where AI-driven insights and decision-making are crucial. Oracle, on the other hand, caters to a broader range of industries with its diverse cloud offerings, including manufacturing, telecommunications, and government sectors.
Integration with Existing Systems: IBM Watson is designed to seamlessly integrate with third-party systems and applications to enhance their AI capabilities. In comparison, Oracle's cloud solutions are often integrated with its own ecosystem of enterprise applications, databases, and infrastructure, providing a more cohesive environment for organizations using Oracle products.
Open Source vs. Proprietary Technology: IBM Watson incorporates a mix of open-source technologies and proprietary algorithms to build its AI solutions, encouraging collaboration and innovation in the AI community. On the contrary, Oracle relies more heavily on proprietary technologies developed in-house, which may limit interoperability with other systems but offer a higher level of control and security for enterprises.
Developer Ecosystem: IBM Watson has a strong developer ecosystem that supports the creation of AI applications and solutions using Watson tools and APIs. In contrast, Oracle focuses on supporting developers within its own ecosystem, providing resources for building applications that leverage Oracle's cloud services and enterprise technologies.
In Summary, IBM Watson and Oracle differ in terms of their focus on AI capabilities, cloud offerings, industry verticals, integration capabilities, technology approaches, and developer ecosystems.
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 IBM Watson
- Api4
- Prebuilt front-end GUI1
- Intent auto-generation1
- Custom webhooks1
- Disambiguation1
Pros of Oracle
- Reliable44
- Enterprise33
- High Availability15
- Expensive5
- Hard to maintain5
- Maintainable4
- Hard to use4
- High complexity3
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Cons of IBM Watson
- Multi-lingual1
Cons of Oracle
- Expensive14
