I find Spark SQL's seamless integration of SQL queries with Spark programs and its use of DataFrames and Datasets particularly valuable. While we mostly stick to traditional T-SQL, Spark SQL brings flexibility to handle large-scale data processing. The ability to write SQL queries, even with minor adjustments for functions like LICA, simplifies our data transformation. Although the syntax differs from traditional SQL, Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline.

With Spark SQL, I would say the best feature is the connectivity and the ease of use with SQL. The team members don't have to learn a new language and can implement complex tasks very easily using only SQL.

We don't need to use the system much and can use Spark features along with the SQL code.

The query language and using it to process large datasets has been the biggest benefit for us.

One of Spark SQL's most beautiful features is running parallel queries to go through enormous data. We can run queries in parallel and retrieve more data and results in aggregation. Spark does this faster than Python.

I find the Thrift connection valuable.

Spark SQL gives us a handful of methods to design queries based on its own syntax and also incorporates the regular SQL syntax within tasks. It's also a good tool for querying and depicting data. It's very good for us and it's helpful that we have access to a lot of good documentation. 

The solution is easy to understand if you have basic knowledge of SQL commands.

Projects sit within the Spark scope and there are multiple options for data sets such as closed, private, or public. 

It is easy to perform analytics because data is contained within each use case. For example, you request data for a particular use case, receive the data link, and import it for analytics. 

The speed of getting data, as our TBs are big and it's a lot of data. 

Overall the solution is excellent.

The solution is continuing to evolve and mature over time.

The performance is one of the most important features. It has an API to process the data in a functional manner. 

This solution is useful to leverage within a distributed ecosystem. 

Data validation and ease of use are the most valuable features.

It is a stable solution.