Apache Spark is one of the extensively used frameworks for handling and processing with Big Data. It also provides various benefits like:
- It is much faster
- Easy to use
- Offers simplicity and virtually runs everywhere
Apache Spark holds built-in mechanisms for SQL, data analysis, machine learning, which makes it very attractive and the most required tools in the IT industry. Spark is written in the Scala programming language, and it has an Application Program Interface (API) for Python, Java, Scala, and R; however, the commonly utilized programming languages with Spark are Python and Scala. So, this is where using Spark with Python, called PySpark gets into the plot.
What is PySpark?
PySpark is a python API for Spark launched by the Apache Spark community to support python with Spark. With PySpark, the integration and working with RDD is very easy. If you need to build Machine Learning pipelines/building ETLs for a data platform, you need to understand the concepts of PySpark. If you have some knowledge on
Python and software libraries like Pandas, then there are no doubt that PySpark will be the most suitable source to build more scalable reports and pipelines.
The advantages of Using PySpark:
In-Memory Computation in Spark:
With its unique In-memory feature, it supports boosting the processing speed. And it saves the data as a cache, where you do not need to obtain data from the disk, which saves your time. PySpark also holds DAG execution, which promotes in-memory computation acyclic flow of data that eventually upshots high speed.
By using PySpark, it is possible to get high-speed data processing (say 10x speed on the disk, 100X speed on memory). It is achievable by decreasing the amount of reading or writing a task to disk.
Dynamic in Nature:
PySpark is very progressive, and it benefits you to create a lateral application being offering with 80 high-level officials.
Fault Tolerance in Spark:
PySpark offers a fault tolerance feature through Spark abstraction-RDD. PySpark is uniquely devised to manage the breakdown of any mode of operation in the batch and to make sure that the data loss is wholly lessened.
Real-Time Stream Processing:
PySpark is much distinct and more reliable than other programming languages, particularly regarding the real-time streaming task. While using Hadoop MapReduce, it has a drawback that it can handle only the stored data and not the real-time data. But when it comes to PySpark Streaming, the disadvantage of real-time handling data has been considerably reduced.
Previously, there was a lot of trouble in handling massive datasets. To simplify this task, some of the versatile tools have been introduced. One of the most prevalent tools among them is Apache Spark that helps to manage the big data very efficiently. There is no dilemma that Python is one of the most extensively used languages among Business professionals. This is because Python is:
- a general-purpose language
- simple and interactive interface
- easy to learn
Therefore combining Spark and Python is an ever best idea to win the big data world. That is what something ideal the Apache Spark community done when they developed up with the PySpark tool.
Who highly benefits from PySpark?
PySpark is the most trusted tool among data scientist professionals to perform data analysis, machine learning and much more on big data. Data Analyst and Data scientist professionals are the ones who primarily profited by using the disseminated processing ability of PySpark. And with PySpark, the process of work right from the initial stage to completion is amazingly smooth and simple. With PySpark, the data scientist professionals can easily cluster and reconstruct the data and can bring back the consolidated data. Hence, PySpark acts as an ideal tool for production and evaluation.