Alteryx Designer Cloud vs. Databricks Lakehouse Platform

For doing ETL and data transformations where your data is already in a cloud environment Alteryx Designer Cloud is ideal. It allows for the continued democratisation of data and analytics to permeate more extensively throughout a business, particularly those that are already in the cloud or have cloud services in place and are comfortable with procurement and consumption of these systems over and above desktop software. The ETL processing for data validation and wrangling uses machine learning to enable highly efficient data manipulation which saves lots of time and this is a distinct advantage over the desktop software. The expansion and integration of other Alteryx cloud services such as ML, Metrics Store and App builder will only strengthen this position

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

• Gives automatic options for code.
• Allows for notes on code.
• Duplicates work and allows for repurposing work.

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

• Hadoop source data tables are presented in a flat searchable list but I would rather see them in the native hierarchy.
• Web interface can be flaky. We often need to refresh the page.

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

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

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.

Our IT group presented Trifacta to me. They picked it out and among the crowd for me. This was way beyond ETL tools, SQL, or any programming language. It's just much faster and because it's sitting on Hadoop you bypass some of the slowdowns typically faced with RDBMS.

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.

• Increased productvity by 5x
• Reduced costs of running and patching desktop and server based software entirely

• 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