Pyspark is an interface used for Apache Spark in Python. It is a Spark library that allows the use of Spark. It allows the user to build spark applications using Python APIs. Spark is an open-source system that uses a cluster computing method. Cluster computing is used in big data solutions. Spark is a very fast tool and designed specifically for fast computation. 

Pyspark being an interface for Apache Spark, provides Py4j library. This library helps Python to easily integrate with Apache Spark. It plays a very major role whenever the work has to be done with a large set of data or when analysing a huge set of data. This is the reason why the Pyspark tool is very popular amongst the data engineers. 

Features of Pyspark:

• In-memory computation
• Lazy evaluation
• Fault tolerant
• Immutability
• Partitioning 
• Persistence
• Coarse grind operations

Other major characteristics of Pyspark are:

• Realtime computation. It mainly focuses on in-memory processing and therefore provides real-time computation on vast amounts of data. It has less latency. 
• It supports multiple languages. Pyspark tool or framework is compatible with many programming languages such as Java, Scala, R and Python. This suitability makes it the preferred choice framework for processing large datasets. 
• Caching and disk constancy. The framework gives a strong caching and good disk constancy. 
• Swift processing. The framework allows its users to achieve high speed data processing ability. This is roughly about 100 times faster in memory and 10 times faster in the disk. 
• Working with RDD. The platform works better with RDD. Python is a programming language that is dynamically typed. This hugely impacts when working with RDD. RDD is used with Python. 

Apache Spark: Apache Spark is an open-source framework that uses distributor cluster-computing. It was designed by Apache Software Foundation. It is an engine used in big data analysis, big data processing and data computation. It is designed to work with high speed, easy to use, framework simplicity, analyse streaming and to run virtually on any platform. It analyses data in real-time. While working with big data, it provides faster computation comparatively. It is faster than the other previous approaches used to work with big data, like MapReduce. The focus feature of Apache Spark framework is that the in-memory cluster computing improves the speed of processing an application.  

Pyspark is preferred for many reasons. Data is generated every second both online and offline. These generated data or already existing data may contain important things such as hidden patterns, unknown corrections, market trends, customers choice and useful business or organization data. All these data will be present in raw form. It is very necessary to extract information from the raw data. A very well developed tool is required to perform various types of operations on the big data. Various tools are available to perform multiple tasks on a vast dataset. A lot of these tools are not very appealing these days. A scalable and flexible tool is preferred to crack big data and extract the required information from the dataset. 

Pyspark framework is used in various real-time scenarios. Data is used in large scale in many industries and analysts work on extracting the data, like in:

• Entertainment industry. It is a popularly growing industry, mostly online streaming these days. Platforms like Netflix, Prime video, and other such online entertainment channels use Apache Spark for analysing customers data in real-time. With this data, they personalize the user's desired top pics in each section. 
• Commercial vertical. This sector uses Apache Spark for real-time data processing. Banks, agencies that are related to the financial sector use Spark to retrieve customers' social media accounts to analyze the data and extract useful insights. This information is used for the credit risk assessment, target advertisements and segment the customer. It is also used in fraud detection and machine learning performances. 
• Healthcare sector. Pyspark is used to understand the patient’s records. It can compare and draw the insights from the previous reports. It can also predict which patient is more likely to face illness after the clinical assessments are over.  
• Trade and E-Commerce segment. Flipkart, Amazon, etc are the most popular ecommerce websites. These sites use Pyspark to target advertisements to its customers. Alibaba uses Apache Spark to provide targeted offers to its customers, to improvise customer experience and also to optimize overall performance. 
• Tourism industry. Apache Spark is used in the tourism industry to advise travelers about traveling packages by comparing hundreds of tourism websites. 

The free PySpark certificate course offered by Great Learning will help you understand the subject, its features and the working of it. It is applied to solve various real-time problems like in e-commerce, trade, etc. Being a very powerful tool for Apache Spark for Python, it is used to work with big data. It helps individuals to have a better hold on Python. You can also learn PySpark for free whenever you want. You will also earn a certificate after the successful completion of the course. Happy learning!