

IBM SPSS Statistics and Databricks compete in the domain of advanced data analytics, with IBM SPSS Statistics having a strong focus on statistical analysis while Databricks emphasizes big data capabilities and machine learning. Databricks appears to have the upper hand in versatility and scalability, especially due to its cloud-based deployment and integration with Apache Spark for enhanced machine learning functionalities.
Features: IBM SPSS Statistics offers extensive modeling techniques including regression and PCA, alongside statistical modeling functions that are essential for comprehensive statistical analysis. It also provides a user-friendly point-and-click interface and built-in functions that are easily accessible. Databricks integrates seamlessly with Apache Spark and offers built-in machine learning libraries, multiple language support in its collaborative notebook environment, and allows for flexible big data processing and analysis capabilities.
Room for Improvement: IBM SPSS Statistics could enhance its data visualization capabilities, improve cloud integration, and provide better automation for ease of use. Users have also mentioned the need for more advanced visualization functions. For Databricks, user feedback suggests improvements in affordability, data governance, and better integration with external visualization tools, as well as enhancing ease of use for non-coding professionals.
Ease of Deployment and Customer Service: IBM SPSS Statistics is primarily an on-premises solution which limits scalability, whereas Databricks offers flexible deployment options in public and hybrid clouds, allowing for seamless scalability with data volumes. In customer service, IBM SPSS Statistics has mixed reviews, with some users noting delays. Databricks receives positive feedback for resolving deployment issues, although some users report occasional service delays.
Pricing and ROI: IBM SPSS Statistics is perceived as expensive with varying license costs but is valued for its extensive statistical capabilities and potential ROI from efficient data management. Databricks is also considered costly, particularly for large data operations; however, its pay-per-use model can be cost-effective for varied workloads, providing good performance and ROI in handling extensive data processing needs.
When it comes to big data processing, I prefer Databricks over other solutions.
For a lot of different tasks, including machine learning, it is a nice solution.
Whenever we reach out, they respond promptly.
As of now, we are raising issues and they are providing solutions without any problems.
I rate the technical support as fine because they have levels of technical support available, especially partners who get really good support from Databricks on new features.
I would rate the scalability of this solution as very high, about nine out of ten.
The patches have sometimes caused issues leading to our jobs being paused for about six hours.
Databricks is an easily scalable platform.
Although it is too early to definitively state the platform's stability, we have not encountered any issues so far.
They release patches that sometimes break our code.
Databricks is definitely a very stable product and reliable.
We use MLflow for managing MLOps, however, further improvement would be beneficial, especially for large language models and related tools.
If I could right-click to copy absolute paths or to read files directly into a data frame, it would standardize and simplify the process.
They're now coming up with their IBI dashboard, and I think they're on the right track to improve that even further.
I'm unsure if SPSS has a commercial offering for big servers, unlike KNIME, which does.
It is not a cheap solution.
Databricks' capability to process data in parallel enhances data processing speed.
The Unity Catalog is for data governance, and the Delta Lake is to build the lakehouse.
The platform allows us to leverage cloud advantages effectively, enhancing our AI and ML projects.
I mainly used it for cross tabs, correlation, regression, chi-squared tests, and similar analyses often seen in published papers.
| Product | Market Share (%) |
|---|---|
| Databricks | 13.9% |
| IBM SPSS Statistics | 4.0% |
| Other | 82.1% |


| Company Size | Count |
|---|---|
| Small Business | 25 |
| Midsize Enterprise | 12 |
| Large Enterprise | 56 |
| Company Size | Count |
|---|---|
| Small Business | 9 |
| Midsize Enterprise | 6 |
| Large Enterprise | 19 |
Databricks offers a scalable, versatile platform that integrates seamlessly with Spark and multiple languages, supporting data engineering, machine learning, and analytics in a unified environment.
Databricks stands out for its scalability, ease of use, and powerful integration with Spark, multiple languages, and leading cloud services like Azure and AWS. It provides tools such as the Notebook for collaboration, Delta Lake for efficient data management, and Unity Catalog for data governance. While enhancing data engineering and machine learning workflows, it faces challenges in visualization and third-party integration, with pricing and user interface navigation being common concerns. Despite needing improvements in connectivity and documentation, it remains popular for tasks like real-time processing and data pipeline management.
What features make Databricks unique?In the tech industry, Databricks empowers teams to perform comprehensive data analytics, enabling them to conduct extensive ETL operations, run predictive modeling, and prepare data for SparkML. In retail, it supports real-time data processing and batch streaming, aiding in better decision-making. Enterprises across sectors leverage its capabilities for creating secure APIs and managing data lakes effectively.
IBM SPSS Statistics is a powerful data mining solution that is designed to aid business leaders in making important business decisions. It is designed so that it can be effectively utilized by organizations across a wide range of fields. SPSS Statistics allows users to leverage machine learning algorithms so that they can mine and analyze data in the most effective way possible.
IBM SPSS Statistics Benefits
Some of the ways that organizations can benefit by choosing to deploy IBM SPSS Statistics include:
IBM SPSS Statistics Features
Reviews from Real Users
IBM SPSS Statistics is a highly effective solution that stands out when compared to many of its competitors. Two major advantages it offers are the wealth of functionalities that it provides and its high level of accessibility.
An Emeritus Professor of Health Services Research at a university writes, "The most valuable feature of IBM SPSS Statistics is all the functionality it provides. Additionally, it is simple to do the five-way analysis that you can in a multidimensional setup space. It's the multidimensional space facility that is most useful."
A Director of Systems Management & MIS Operations at a university, says, “The SPSS interface is very accessible and user-friendly. It's really easy to get information from it. I've shared it with experts and beginners, and everyone can navigate it.”
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