

IBM Watson Studio and Dremio compete in the data handling and analysis sector. IBM Watson Studio has the upper hand with its advanced machine learning capabilities and integration with IBM tools, making it a comprehensive solution for enterprises.
Features: IBM Watson Studio is praised for its machine learning capabilities, seamless data importation, and integration with other IBM tools. It offers cloud-based solutions that are comprehensive for data modeling and processing. Dremio stands out with its compatibility with various data storage systems, unique query capabilities, and efficient data management without the need for additional resources.
Room for Improvement: IBM Watson Studio needs to improve user accessibility, provide clearer deployment paths, and enhance support according to user feedback. The complexity of its setup requires a more intuitive design. Dremio faces challenges with certain SQL queries, performance issues, and needs improved connectors while simplifying complex tasks like building trees. Enhancing Delta connector support and flexibility are areas of improvement.
Ease of Deployment and Customer Service: IBM Watson Studio primarily operates on public and on-premises clouds, noted for its reliable technical support with varying user experiences. Dremio offers flexible deployment options including public, hybrid, and on-premises, highlighting its versatility in cloud solutions for data management.
Pricing and ROI: Watson Studio is considered expensive but with justifiable features that yield positive ROI through scalability and efficiency. Dremio is seen as a cost-effective alternative to tools like Snowflake, although some licensing costs are high. Both solutions provide positive ROI from enhanced data handling and process automation.
Dremio surely saves time, reduces costs, and all those things because we don't have to worry so much about the infrastructure to make the different tools communicate.
The product offers a significant return on investment through its scalability and integration capabilities.
My customers have seen returns on investment through increased efficiency, automated calculations, improved accuracy in pricing, and reduced staffing needs due to the automation.
We have had to reach out for customer support many times, and they respond, so they are pretty supportive about some long-term issues.
The community access is weak, which limits the ability to engage in discussions and find documentation and examples of similar cases effectively.
The support quality depends on the SLA or the contract terms.
Dremio's scalability can handle growing data and user demands easily.
Internally, if it's on Docker or Kubernetes, scalability will be built into the system.
Watson Studio is very scalable.
I rate IBM Watson Studio seven out of ten for scalability because while it scales, it requires significant resources to do so, making it expensive compared to some competitors.
I rate Dremio a nine in terms of stability.
Expertise in optimization is necessary to manage such issues effectively.
Starburst comes with around 50 connectors now.
I see that many times the new versions of Dremio have not fixed old bugs, and in some new versions, old problems that were previously fixed come back again, so I think the upgrade part could use improvement.
It should be easier to get Arctic or an open-source version of Arctic onto the software version so that development teams can experiment with it.
IBM should work on optimizing the user interface and enhancing the product's accessibility for medium and small enterprises.
One area that could be improved is the backup and restoration of the database and the overall database configuration.
I wish learning IBM Watson Studio could be easier and more gradual, as it is a complex task.
IBM Watson Studio is considered rather expensive, with a rating of six or seven.
Having everything under one system and an easier-to-work-with interface, along with having API integrations, adds significant value to working with Dremio.
Dremio has positively impacted my organization as nowadays we are connected to multiple databases from multiple environments, multiple APIs, and applications, and Dremio organizes everything in an amazing way for me.
You just get the source, connect the data, get visualization, get connected, and do whatever you want.
This capability saves a significant amount of time by automating processes that typically involve manual work, such as data cleaning, feature engineering, and predictive analytics.
The best features IBM Watson Studio offers are that it is good for big and complex organizations, it is multi-cloud, it has an on-prem facility, and it also has strong visual tools.
It integrates well with other platforms and offers good scalability.
| Product | Market Share (%) |
|---|---|
| Dremio | 2.6% |
| IBM Watson Studio | 2.2% |
| Other | 95.2% |

| Company Size | Count |
|---|---|
| Small Business | 1 |
| Midsize Enterprise | 5 |
| Large Enterprise | 5 |
| Company Size | Count |
|---|---|
| Small Business | 12 |
| Midsize Enterprise | 1 |
| Large Enterprise | 4 |
Dremio offers a comprehensive platform for data warehousing and data engineering, integrating seamlessly with data storage systems like Amazon S3 and Azure. Its main features include scalability, query federation, and data reflection.
Dremio's core strength lies in its ability to function as a robust data lake query engine and data warehousing solution. It facilitates the creation of complex queries with ease, thanks to its support for Apache Airflow and query federation across endpoints. Despite challenges with Delta connector support, complex query execution, and expensive licensing, users find it valuable for managing ad-hoc queries and financial data analytics. The platform aids in SQL table management and BI traffic visualization while reducing storage costs and resolving storage conflicts typical in traditional data warehouses.
What are Dremio's most valuable features?Dremio is primarily implemented in industries requiring extensive data engineering and analytics, including finance and technology. Companies use it for constructing data frameworks, efficiently processing financial analytics, and visualizing BI traffic. It acts as a viable alternative to AWS Glue and Apache Hive, integrating seamlessly with multiple databases, including Oracle and MySQL, offering robust solutions for data-driven strategies. Despite some challenges, its ability to reduce data storage costs and manage complex queries makes it a favorable choice among enterprise users.
IBM Watson Studio provides tools for data scientists, application developers and subject matter experts to collaboratively and easily work with data to build and train models at scale. It gives you the flexibility to build models where your data resides and deploy anywhere in a hybrid environment so you can operationalize data science faster.
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