Databricks Lakehouse Platform vs. Mode Analytics

If you need a managed big data megastore, which has native integration with highly optimized Apache Spark Engine and native integration with MLflow, go for Databricks Lakehouse Platform. The Databricks Lakehouse Platform is a breeze to use and analytics capabilities are supported out of the box. You will find it a bit difficult to manage code in notebooks but you will get used to it soon.

Incentivized

Mode is a clear category winner for how it easy it is to go from interactive analysis to professional quality reporting. You can easily do all your SQL and quite a bit of R/Python. If you want to quickly go from your database to simple charts, this is your pick. If you need to do heavier data modeling, scheduling jobs, or ML, you'll need something else, like Databricks. In my opinion, the quality of visualizations and some developer/analyst features make Databricks an unacceptable substitute.

Incentivized

• Process raw data in One Lake (S3) env to relational tables and views
• Share notebooks with our business analysts so that they can use the queries and generate value out of the data
• Try out PySpark and Spark SQL queries on raw data before using them in our Spark jobs
• Modern day ETL operations made easy using Databricks. Provide access mechanism for different set of customers

Incentivized

• It allows me to undertake data visualization tasks by creating really insightful charts and dashboards
• It allows for easy sharing and work collaboration on data analysis tasks
• Mode has an easier to use SQL editor as well
• I love how it organizes all our SQL data tables into easy-to-view data we can draw insights on

Incentivized

• Connect my local code in Visual code to my Databricks Lakehouse Platform cluster so I can run the code on the cluster. The old databricks-connect approach has many bugs and is hard to set up. The new Databricks Lakehouse Platform extension on Visual Code, doesn't allow the developers to debug their code line by line (only we can run the code).
• Maybe have a specific Databricks Lakehouse Platform IDE that can be used by Databricks Lakehouse Platform users to develop locally.
• Visualization in MLFLOW experiment can be enhanced

Incentivized

• Date ranges aren't always the most intuitive to set, strangely
• Faster refresh rates would be ideal
• Would love more dynamic mobile device integration (alerts, etc)

Incentivized

Because it is an amazing platform for designing experiments and delivering a deep dive analysis that requires execution of highly complex queries, as well as it allows to share the information and insights across the company with their shared workspaces, while keeping it secured.

in terms of graph generation and interaction it could improve their UI and UX

Incentivized

For the advanced user already familiar with Python. It is just the same tool, already managed and connected to the data.

Incentivized

One of the best customer and technology support that I have ever experienced in my career. You pay for what you get and you get the Rolls Royce. It reminds me of the customer support of SAS in the 2000s when the tools were reaching some limits and their engineer wanted to know more about what we were doing, long before "data science" was even a name. Databricks truly embraces the partnership with their customer and help them on any given challenge.

Mode has dedicated agents who are online in a chatroom, ready to take on any questions and provide helpful feedback.

Incentivized

Compared to Synapse & Snowflake, Databricks provides a much better development experience, and deeper configuration capabilities. It works out-of-the-box but still allows you intricate customisation of the environment. I find Databricks very flexible and resilient at the same time while Synapse and Snowflake feel more limited in terms of configuration and connectivity to external tools.

We used both Qlik and Mode at our organization. The mode was used a quick analysis SQL tool while Qlik was our enterprise BI solution.

Incentivized

• The ability to spin up a BIG Data platform with little infrastructure overhead allows us to focus on business value not admin
• DB has the ability to terminate/time out instances which helps manage cost.
• The ability to quickly access typical hard to build data scenarios easily is a strength.

Incentivized

• Extremely positive. It's a key feature in our application.
• Were it not for the account management bit, Mode would be hands-down our recommendation for anyone looking for cloud BI.

Incentivized