Azure Databricks vs. IBM SPSS Modeler

Suppose you have multiple data sources and you want to bring the data into one place, transform it and make it into a data model. Azure Databricks is a perfectly suited solution for this. Leverage spark JDBC or any external cloud based tool (ADG, AWS Glue) to bring the data into a cloud storage. From there, Azure Databricks can handle everything. The data can be ingested by Azure Databricks into a 3 Layer architecture based on the delta lake tables. The first layer, raw layer, has the raw as is data from source. The enrich layer, acts as the cleaning and filtering layer to clean the data at an individual table level. The gold layer, is the final layer responsible for a data model. This acts as the serving layer for BI For BI needs, if you need simple dashboards, you can leverage Azure Databricks BI to create them with a simple click! For complex dashboards, just like any sql db, you can hook it with a simple JDBC string to any external BI tool.

Incentivized

Modeler is well suited for understanding consumer data. The ability to create a prediction and then to understand what is driving that prediction is strong in Modeler. Modeler is closely aligned with the CRISP-DM data mining approach meaning it is not just the 'doing' but also the theoretical background behind the development of data mining models.

Incentivized

• Unity Catalog
• Data Federation in Lakehouse Architecture
• Integration of Mosaic AI in the SQL Layer

Incentivized

• A very nice and easy to use interface.
• A great variety of analytics, from statistical calculation to data validation and predictive statistics.
• Has a steep learning curve.

Incentivized

• Their pipeline workflow orchestration is pretty primitive. Lacks some common features
• Workspace UI and navigation requires steep learning curve
• Personally, I am not fond of their autosave feature. Its dangerous for production level notebooks scripts

Incentivized

• Some Analyses aren't there out of the box but can be added through open languages like R and Python.
• Graphs could be better.
• Unable to read data stored in OLAP databases

Incentivized

Great for what we use day to day and does what we need it to do. Cost management is not fully developed across the UX and gets expensive very quickly for developing projects. Integrated very well with our Microsoft stack and can be worked on collaboratively which works well for us.

Incentivized

The ability to do predictive modeling, text analytics for both structured & unstructured data, decision management, optimization, and support for various data sources

Incentivized

The online support board is helpful and the free add ons are incredibly appreciated.

Incentivized

I have found Azure Databricks to be much better than Snowflake for handling bigger, diverse data types. Snowflake is much simpler and better for smaller warehousing. The real time processing is much better in Azure Databricks and we have much more language options. Snowflake is more expensive but simpler to use. Both are great for different needs.

Incentivized

We additionally use SAS Data Miner as a toolkit. Compared to SAS Data Miner, the SPSS Modeler is a good competitor. SAS probably is more integrated in the market for a visual-based code for data science activities. However, I don't think it offers anything better than SPSS, and I really like several of the helpful components for usability for SPSS like peaks into nodes.

Incentivized

• Helped reduce time for collecting data
• Reduced cost in maintaining multiple data sources
• Access for multiple users and management of users/data in a single platform

Incentivized

• I am able to study and work from home sustainably
• I can help others have a high quality university education experience to graduate confident and competent to meet gaps in the wider community
• Market research for my business
• Help other small businesses to create viable and high quality products and services
• Contribute to research projects: ethical, high quality data analyses and interpretation

Incentivized