Azure Databricks vs. SAS Enterprise Miner

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

Text Miner option is very useful to uncover trending themes in very large data sets.

Incentivized

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

Incentivized

• Developing and evaluating ensemble models.
• A very transparent interface.

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

• One of the major flaws is that the tool is basically an interface to SAS/STAT code. It generates code in the background and runs it. Because of that, some errors are warning might be a little difficult to understand for users who aren't proficient with SAS code.
• R integration is nice but I would like to see the possibility to integrate even more statistical models different than SAS. That would allow for better performance optimization when really required.
• The light client is java based and a little heavy on the OS. It would be nice to get a web-based version of the tool instead of the java one.

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

I have contacted SAS twice in the past year and they have been super responsive both times. They solved my problem. I am also registered for an in-person class next month and they called today to tell me that it will be an online-only session. They apologized for the change and registered me for the online version. Super helpful!

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

For those that are used to the SAS ecosystem, SAS Enterprise Miner is a massive move in the right direction. It makes doing analytics much more enjoyable. It is more user-friendly than Spotfire or Kinesis and seems to produce better results overall. SAS Enterprise Miner seems to be written by analysts for analysts.

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

• It has a positive ROI to our business, as our sales lead rate increased after we started recommending SAS EM.
• Our business operation numbers improved after we introduced SAS EM and started using predictive analytics for our customer retention and customer chain prediction.
• The statistical modelling for the risk controls in our financial department helped to reduce the related residual risk.

Incentivized