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As  apache spark  in  Hadoop   works with the data that is structured and semistructured so spark SQL documentation behaves as an interface between it. Structured data is all those data which has the schema like JSON, hive tables, and Parquet. To know a full set present in a field is known as the schema for every record. And when there is no separation is done between schema and the data provided it is known as semistructured. 3 capabilities for the data which are structured and unstructured in best Spark SQL tutorial are below:

1.The data frame abstraction is being provided for java, python, and scala which simplifies all the work with all the data. Data frames are nothing but similar to some tables in the relational database.
2.For the different structured formats, the set and the different types of the data can be read and write.
3.By using SQL a data query can be created, from the inner part of the program of Spark and also all the external tools which connect with Spark SQL with the help of the connectors like JDBC and ODBC in the standard database.



 Apache kafka  and  apache spark  are a part of  Hadoop   similarly Spark SQL is a part of Apache spark.
If a person wants to get Spark SQL syntax in the applications list then some of the additional things like the library dependencies are also required to be installed. And is until now anonymous that Spark SQL is built on or not with the help of Apache Hive. whereas when the Spark SQL is being downloaded it is present in the form of binary and it would be built-in with supporting hive.


Inside the Spark application, the Spark SQL is being best used. Which helps in combining of the ability to load and have the data related to query which will simultaneously combine it python, java, and scala.


The relational database in the form of the table is something to similar to data frames. Fro a whole row of objects data frame represents RDD. the schema of the whole Row would be known in a data frame. Whereas the data stored in the data frames are more efficient than that of the RDD which takes advantage by getting the schema.


For different types of columns, Caching becomes the very important part as the data frame knows all the columns even.