WHAT IS SPARK SQL
As apache spark in Hadoop works with
the data that is structured and semi structured 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 semi structured. 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.
SPARK SQL ARCHITECTURE
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.