Is PySpark SQL Still Relevant?
SQL plays an essential role in today’s big data workloads and is widely used to work with data in the cloud. PySpark SQL is a popular Python library for Apache Spark that facilitates data extraction and analysis using SQL. But Snowpark (a new developer framework from Snowflake) is challenging the continued relevance of PySpark SQL. Let’s take an in-depth look at both and then explore how Snowpark is helping data engineers, data scientists, and developers to work with data more efficiently using their favorite programming languages and tools.
What Is PySpark SQL?
PySpark is a module in Spark that forms a connection between Python and Spark, the popular analytics engine used in many data engineering, data science, and machine learning applications. It also includes the Python shell, used for interactive data analysis in distributed environments. With PySpark, developers can write applications and analyze data in Spark using Python.
PySpark SQL is a Spark library for working with structured and semi-structured data. This library allows SQL queries on massive data sets, playing the role of a distributed SQL query engine. It also allows the use of a data structure called a DataFrame, a programming abstraction that organizes data into a two-dimensional format that resembles a spreadsheet. This versatile, user-friendly way to store and work with data is highly useful in modern data analytics.
How PySpark SQL Works
PySpark SQL is built around PySpark, the interface that allows developers to use Python APIs to write Spark applications. Here’s why it’s a popular tool for using Python in the Spark ecosystem.
Leverages Apache Spark for data processing and analytics tasks
Written in Scala, Spark is an analytics engine that’s optimized for large-scale data processing. Spark’s use of in-memory processing makes it extremely fast. PySpark joins the simplicity and power of Python with the speed and reliability of Spark.
Integrates with the Python ecosystem
Python has an enormous number of libraries and frameworks that help programmers accelerate the development process. They contain prewritten code that helps developers create faster, more efficient workflows for a variety of big data use cases.
Boasts a large community of active users
Python has a dedicated and rapidly growing number of users. Python’s vibrant community has built a wealth of resources that make it easy for new and advanced programmers to get the most out of Python.
Joins relational processing with the PySpark SQL module
The PySpark SQL module joins relational processing with Spark's functional programming API. This makes it possible to leverage the benefits of relational processing such as using declarative queries and optimizing data storage.
Why Snowpark Is a Better Choice
While PySpark SQL served a vital function for many years, its relevance has diminished with the introduction of Snowpark. But PySpark and PySpark Module have major limitations:
Spark is Scala based; internal function changes are not possible for Python applications
The above point also results in PySpark being up to 10x slower than Scala for Spark
PySpark has overall lowing programming efficiency and is hard to express
Uses non-standard SQL
Snowpark unites some of today’s most popular programming languages (including Python, Java, and Scala) with familiar DataFrame and custom function support. Snowpark offers developers the freedom to build powerful and efficient pipelines, machine learning (ML) workflows, and data applications with the performance, ease of use, governance, and security provided by the Snowflake Data Cloud. Snowpark brings more programmability to the Snowflake platform beyond just SQL. Developers of different languages can collaborate on the same data, in the same platform without moving data around. Here’s why many data engineers, data scientists, and developers are making the switch.
Improved collaboration
When all your users are on a single platform, teams are free to collaborate on the same single copy of data while natively supporting everyone’s programming language of choice.
Teams can develop flexible data pipelines with support for popular programming languages.
Faster time to market
Snowpark enables teams to operationalize data more quickly so they can work efficiently with optimized, scalable workflows. Teams are using Snowpark to build scalable, optimized pipelines, apps, and ML workflows with near-zero maintenance powered by Snowflake’s elastic performance engine.
Lower operational costs
Snowflake makes it easy to turn computer resources on and off so you only pay for what you use. Take advantage of cost-effective compression in Snowflake to store near-unlimited amounts of data. With linear cost scalability, organizations can grow their analytics and development infrastructure as needed and avoid paying for excess capacity.
Reduced data security and compliance risks
It’s simple to integrate governance and security, extending Snowflake’s fully managed, enterprise-grade governance controls and security features across all your workflows. Consistently enforce governance and security policies from a single platform while managing libraries with full governance control.
Continued access to open-source libraries
With Snowpark, developers can access their favorite external libraries and use them in Snowflake. Speed up Python-based workflows with seamless access to open-source packages and package managers via Anaconda Integration.
Snowpark in Action
Snowpark is accelerating the pace of innovation. Here are a few ways teams are using this developer framework.
Data science and machine learning
Data programmability advancements in Snowpark provide greater flexibility and
extensibility. Data scientists can leverage their favorite programming languages, including Python, to access, visualize, and process data as part of their ML workflows. Snowpark use cases for data science include feature engineering, ML model inference, end-to-end ML in Snowflake with SQL, and more.
Data-driven applications
Using Snowpark, developers are creating dynamic, data-heavy apps that run directly on Snowflake. With access to compute resources optimized for high speed on data of any size, data-intensive apps run quickly and efficiently. In addition, by using Snowpark with Snowflake’s native app, developers can leverage industry-leading security and governance to protect user data.
Complex data transformations
Snowpark streamlines complete data transformations and ETL workloads. Developers can work with data directly in Snowflake, avoiding the need to transfer data to an external environment. Its built-in functional programming paradigm also makes it possible for developers to evaluate their code for readability and simple unit tests.
Advancing Python Development Using Snowflake
By harnessing Python’s familiar syntax and thriving ecosystem of open-source libraries, Snowflake Snowpark empowers data engineers, developers, and data scientists to explore and process data where it lives. The Snowpark library is a simple, easy-to-use API for querying and processing data in a data pipeline. Users can pair their language of choice (including Python, Java, or Scala) with familiar DataFrame and custom function support to build powerful and efficient pipelines, ML workflows, and data applications while working inside Snowflake’s Data Cloud.