What is PySpark and who uses it? This is a common question many data scientists and engineers may ask. Let’s explore where this Python module comes from and how you can make use of it.
What Is PySpark And Who Uses It?
PySpark is a high-level application programming interface (API) of Apache Spark – the analytics framework for big data processing. It allows people with expertise in Python to write programs in Spark, while the PySpark shell is an amazing tool for interactive data analytics in distributed environments.
Most amazing features of Spark are supported by PySpark, including Spark Core, Spark SQL, MLib, and Streaming.
This is the underlying machine that every functionality of Spark depends on. Capabilities it provides include in-memory computing and RDDs (Resilient Distributed Datasets).
This is the primary module of PySpark, providing methods and functions for structured data processing. DataFrame, one of the most important data abstractions in Spark, belongs to this module. Additionally, this engine can also process distributed SQL queries.
Convert a DataFrame from Pandas to PySpark only take a simple statement:
Pandas is a familiar tool to many scientists and engineers. Its data manipulation capabilities when it comes to time series and numerical tables are top-notch.
Spark draws much of its inspiration from Pandas. In fact, its DataFrame has the same name and many design features as Pandas’ DataFrame – its tabular data objects.
To make interactions between those two modules a breeze, PySpark comes with a built-in Pandas API. You can scale your workload, transferring it from Pandas to a Spark’s cluster.
Many aspects of PySpark don’t require a steep learning curve if you are already good at Pandas. The transition is smooth and doesn’t result in much overhead.
You can take advantage of this integration and develop a single codebase for both frameworks: one for testing small datasets in Pandas and one for distributed large-scale datasets in PySpark.
PySpark supports streaming analytics by using the fast scheduling ability of Spark Core. You can create powerful analytical and interactive applications while inheriting the fault tolerance and ease of use characteristics of Spark.
Spark Streaming processes small batches of data and carries out RDD transformations on them. This approach allows the same codebase designed for batch analytics to perform data stream processing, making lambda architecture easy to implement.
AI/ML is on the rise, and with MLib, scalable applications in this field are possible with Spark.
Built on top of Spark Core, this distributed machine-learning framework is much faster than disk-based implementations. And the best part is that you can create and fine-tune your models and pipelines by using only high-level APIs in PySpark.
PySpark Use Cases
Spark and its APIs, including PySpark, have gained a mainstream presence in recent years. From Fortune 500 companies to startups, this analytics engine has been adopted in several industries:
- Finance: Institutions are using PySpark to gain insights into the market and their customers. It helps banks and other service providers with customer segmentation, targeted advertising, and risk assessment.
- E-Commerce: PySpark can analyze transactions in real-time. And by accessing different sources of data, retailers can make better recommendations to their customers.
- Healthcare: Spark has become the core component of several healthcare applications. It can carry out patient records analysis on a large scale, helping providers with scheduling and even diagnosis.
So, what is PySpark and who uses it? It is an API for Spark – a scalable data analytics engine. It comes with all the benefits of Spark and Python. Check out this guide to get the hang of Spark and PySpark.