

IBM SPSS Statistics and Dremio both compete in the data analysis and management market. While IBM SPSS is preferred for its strong statistical and modeling features, Dremio holds an advantage in data integration and management across diverse storage solutions.
Features: IBM SPSS Statistics provides extensive modeling techniques, including regression, PCA, and Bayesian statistics. It offers support for a wide array of statistical models and machine learning algorithms. It also allows users to create custom tables for enhanced data analysis processes. Dremio allows seamless integration with multiple data sources and is notable for its data lineage and query capabilities. It provides a flexible environment that enhances the management of data stored in various locations and integrates effortlessly with most data storage platforms.
Room for Improvement: IBM SPSS users suggest enhancement in data visualization tools, more intuitive interfaces, and better big data support. Reducing the complexity of user programming and improving documentation are also recommended. Dremio users report challenges with Delta connector support, performance in large queries, and issues integrating with specific data platforms. They also indicate a need for more efficient SQL generation and data processing improvements.
Ease of Deployment and Customer Service: IBM SPSS predominantly operates on-premises, which ensures control but can be resource-intensive. Customer service experiences vary, with some users suggesting improvements in technical support responsiveness. Dremio offers deployment across public and hybrid cloud setups, enabling greater flexibility. However, users have experienced slower responses from customer service when handling technical issues, indicating a need for smoother support systems.
Pricing and ROI: IBM SPSS Statistics is often viewed as expensive, posing challenges for educational institutions despite its strong ROI in data analytics. The high cost is sometimes justified by its features, but users desire more flexible pricing. Dremio, although considered expensive in licensing, is viewed as cost-effective compared to competitors like Snowflake and is appreciated for its scalability. Users believe it offers positive ROI when used effectively.
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.
I would rate customer support a ten out of ten.
We contact the technical support at least weekly; currently, it's more or less daily because we face performance issues, crashes of queries sometimes, and we also need support for configuration of the query engines and so on.
We have had to reach out for customer support many times, and they respond, so they are pretty supportive about some long-term issues.
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.
Dremio's scalability depends on your hardware and your deployment.
I rate Dremio a nine in terms of stability.
We refactored the query, and that worked, but it still doesn't make you confident to go to production with a product that might crash depending on the query you send it.
Starburst comes with around 50 connectors now.
Before we wrap up, I want to add that Dremio is lacking a semantic layer in a manner that the user can just choose dimensions, metrics, and filters to query the data.
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.
I believe that the owners of IBM SPSS Statistics should think about improving the package itself to be able to treat unstructured data.
It does not handle very large data sets well. When there are 100,000 respondents, it does not manage effectively and crashes more often when the data set becomes very large or while merging yearly waves such as 2018, 2019, 2020 to 2026.
I'm unsure if SPSS has a commercial offering for big servers, unlike KNIME, which does.
I chose eight out of ten because Dremio is expensive for the Enterprise version, particularly regarding licensing.
I had a good experience with pricing, as the pricing seems okay to me and the client.
Dremio does have a core of the engine that is open-source, but all the important features for the enterprise version are under license.
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.
Multiple data sources can be queried in one query, and it is not just a query engine.
Predictive analytics is the most important part of analytics.
IBM SPSS Statistics provides excellent data visualization features that other tools do not have.
I mainly used it for cross tabs, correlation, regression, chi-squared tests, and similar analyses often seen in published papers.
| Product | Mindshare (%) |
|---|---|
| Dremio | 2.0% |
| IBM SPSS Statistics | 3.3% |
| Other | 94.7% |


| Company Size | Count |
|---|---|
| Small Business | 3 |
| Midsize Enterprise | 6 |
| Large Enterprise | 5 |
| Company Size | Count |
|---|---|
| Small Business | 9 |
| Midsize Enterprise | 7 |
| Large Enterprise | 20 |
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 SPSS Statistics is renowned for its intuitive interface and robust statistical capabilities. It efficiently handles large datasets, making it essential for data analysis, quantitative research, and business decision-making.
IBM SPSS Statistics offers extensive functionality supporting both beginners and experts. It is used for data analysis across industries, accommodating advanced statistical modeling such as regression, clustering, ANOVA, and decision trees. Users benefit from its quick model building and ease of use, which are indispensable in data exploration and decision-making. Room for improvement includes charting, visualization, data preparation, AI integration, automation, multivariate analysis, and unstructured data handling. Enhancements in importing/exporting features, cost efficiency, interface improvements, and user-friendly documentation are sought after by users looking for alignment with modern data science practices.
What are IBM SPSS Statistics' most notable features?IBM SPSS Statistics is implemented broadly, including academic research for in-depth studies, business analytics for informed decision making, and in the social sciences for comprehensive data exploration. Organizations utilize its advanced features like AI integration and automated modeling across sectors to gain actionable insights, streamline data processes, and support research initiatives.
We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.