What is PySpark?
PySpark is the Python API written in python to support Apache Spark. Apache Spark is a distributed framework that can handle Big Data analysis. Apache Spark is written in Scala and can be integrated with Python, Scala, Java, R, SQL languages. Spark is basically a computational engine, that works with huge sets of data by processing them in parallel and batch systems.
Who can learn PySpark?
Python is becoming a powerful language in the field of data science and machine learning. Through its library Py4j, one will be able to work with Spark using python. Python is a language that is widely used in machine learning and data science. Python supports parallel computing.
The prerequisites are
- Programming knowledge using python
- Big data knowledge and framework such as Spark
One who wants to work with Big Data is the suitable candidate for PySpark.
Advantages of PySpark:
- Easy Integration with other languages: PySpark framework supports other languages like Scala, Java, R.
- RDD: PySpark basically helps data scientists to easily work with Resilient Distributed Datasets.
- Speed: This framework is known for its greater speed compared with the other traditional data processing frameworks.
- Caching and Disk persistence: This has a powerful caching and disk persistence mechanism for datasets that make it incredibly faster and better than others.
Programming with PySpark:
Resilient Distributed Datasets – these are basically datasets that are fault-tolerant and distributed in nature. There are two types of data operations: Transformations and Actions. Transformations are the operations that work on input data set and apply a set of transform method on them. And Actions are applied by direction PySpark to work upon them.
Data frame is a collection of structured or semi-structured data which are organized into named columns. This supports a variety of data formats such as JSON, text, CSV, existing RDDs and many other storage systems. These data are immutable and distributed in nature. Python can be used to load these data and work upon them by filtering, sorting and so on.
In Machine learning, there are two major types of algorithms: Transformers and Estimators. Transforms work with the input datasets and modify it to output datasets using a function called transform(). Estimators are the algorithms that take input datasets and produces a trained output model using a function named as fit().
Without Pyspark, one has to use Scala implementation to write a custom estimator or transformer. Now, with the help of PySpark, it is easier to use mixin classes instead of using scala implementation.