Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline.
Spark SQL is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. There are several ways to interact with Spark SQL including SQL and the Dataset API. When computing a result the same execution engine is used, independent of which API/language you are using to express the computation. This unification means that developers...
Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline.
I find the Thrift connection valuable.
One of Spark SQL's most beautiful features is running parallel queries to go through enormous data.
The team members don't have to learn a new language and can implement complex tasks very easily using only SQL.
Certain data sets that are very large are very difficult to process with Pandas and Python libraries. Spark SQL has helped us a lot with that.
The solution is easy to understand if you have basic knowledge of SQL commands.
Offers a variety of methods to design queries and incorporates the regular SQL syntax within tasks.
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.
The performance is one of the most important features. It has an API to process the data in a functional manner.
The speed of getting data.
Overall the solution is excellent.
The stability was fine. It behaved as expected.