Likelihood to Recommend
If you need a managed big data megastore, which has native integration with highly optimized
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.
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- Don't want to pay
$1,000 / seat? Use Streamlit - Want fully custom views and navigation? Use Streamlit - Want access to Machine Learning and not just your dev team? Use Streamlit - Want to keep things internal and secure? Use Streamlit - Want your Data Science team to be able to crank out projects quickly? Use Streamlit - Sick of Jupyter Notebooks and Business Leaders not understanding them? Use Streamlit Our D.S. strategy has moved completely to delivering pages in Streamlit. I can hand an executive a Jupyter notebook and it'll get lost in translation. I can give them sign-in access to a page and they can answer all of their own "What-If?" questions! We've used Streamlit to productize our Data Science and Machine Learning capabilities.
Read full review Pros 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 Read full review Incredibly Easy Customizable Quick and powerful Read full review Cons 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 Read full review Recent Security issues (they quickly released an update to combat this though...) Requires a bit of HTML knowledge to really customize. If you're going quick, you don't need HTML though. Streamlit commands will pump your page out fast. Read full review Usability
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 Read full review Support Rating
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.
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, 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
feel more limited in terms of configuration and connectivity to external tools.
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I started using Streamlit when it first came out and thought it was really useful and powerful. A few years later and they've really hit their stride! The features / widgets / materials they provide have been well researched, well designed, and well implemented. I will take Streamlit to any future companies I go to as well as be a strong promoter wherever I'm currently at. It's free. It's easy to use. It is really powerful. Sure? You could go pay for a larger system but your Data Science team should be able to handle Streamlit easily. I'd argue a non-technical person spending a few weeks in python could pick up Streamlit really quickly.
Read full review Return on Investment 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. Read full review I've scaled my team 2x since using Streamlit. We show off actual results that users can play with We're building a customer facing page that we're going to monetize. Incredible amounts of visibility into my team and what we're accomplishing. Read full review ScreenShots