PySpark is the Python API that supports Apache Spark. Apache Spark is a open-source, distributed framework that is built to handle Big Data analysis. Spark is written in Scala and integrates with Python, Scala, SQL, Java,, and languages. It acts as computational engine that processes very large data sets in batch and parallel systems. Python is a general-purpose programming language that uses language constructs and object-oriented paradigms to help programmers write clean, highly logical code for a wide range of projects and functions. Python is popular for machine learning- and data analytics-intensive projects.
Who Should Learn PySpark?
Python is a powerful tool for data scientists developing machine learning, data analysis, and AI projects. With PySpark's Py4j library, programmers that work closely with data science projects can easily work with Spark using Python.
Advantages of PySpark:
Simple integration with other languages, including Scala, Java, and R
Powerful caching and disk persistence
Helps data scientists work more efficiently with Resilient Distributed Datasets (RDD)
Faster speed vs.with the other data processing framework
PySpark SQL is an abstraction module over the PySpark Core that is deployed for processing both semi-structured and structured data sets. Pyspark SQL also has an API that reads data from different files formats. This allows it, for example, to use both SQL and HiveQL.
Snowflake and PySpark
Snowflake works with both Python and Spark, allowing developers to leverage Pyspark capabilities in the platform. The Snowflake Connector for Spark (“Spark connector”) brings Snowflake into the Apache Spark ecosystem, enabling Spark to read data from, and write data to, Snowflake.The Snowflake Connector for Python provides an interface for developing Python applications that can connect to Snowflake and perform all standard operations.