Question

List the functions of Spark SQL. Spark SQL is capable of:

a) Processing structured and semi-structured data
b) Running real-time data processing
c) Storing data in a NoSQL database
d) Generating machine learning models

Answer 1

Spark SQL handles both structured and semi-structured data, and can run real-time data processing. It does not store data in NoSQL databases or generate machine learning models, as these tasks are handled by other components or technologies.

Step-by-step explanation:

Functions of Spark SQL

Apache Spark SQL is a component of the open-source Apache Spark framework that supports processing structured and semi-structured data. As such, it can:

• Process structured and semi-structured data, integrating with multiple data sources including JSON, Hive, Avro, etc.
• Run real-time data processing, executing SQL queries on streaming data.

However, Spark SQL is not designed for:

• Storing data in a NoSQL database. Instead, it reads from and writes to various data sources.
• Generating machine learning models. While Spark SQL can prepare and manipulate data, model generation is typically done using the Spark MLlib component.

Spark SQL's combination of SQL with Spark's functional programming API gives it powerful capabilities in data processing and analytics.