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What is PySpark?
PySpark is the Python API for Apache Spark written in Python. Apache Spark is a distributed framework for Big Data analysis. Apache Spark is written in Scala and can be used with Python, Scala, Java, R, and SQL. Spark is essentially a computational engine that works with massive amounts of data in parallel and batch systems.
Who can learn PySpark?
Python is quickly becoming a powerful language in data science and machine learning. One will be able to work with Spark in Python using Py4j’s library. Python is a programming language widely used in machine learning and data science. Python allows for parallel computing.
The requirements are :
- Big data knowledge and framework such as Spark
- Programming knowledge using python
PySpark is a good fit for someone who wants to work with big data.
Programming with PySpark:
RDD:
Resilient Distributed Datasets are datasets that are fault-tolerant and distributed in nature. Data operations are classified into two types: transformations and actions. Transformations are operations that take a set of input data and apply a set of transform methods to it. And Actions are used by instructing PySpark to work on them.
Machine learning:
There are two types of algorithms in machine learning: transformers and estimators. Transforms use a function called transform() to work with input datasets and modify them into output datasets. Estimators are algorithms that take input datasets and generate a trained output model using a function called fit ().
Data frames:
A data frame is a grouping of structured or semi-structured data organised into named columns. This supports a wide range of data formats, including JSON, text, CSV, existing RDDs, and a variety of other storage systems. These are immutable and distributed data. Python can be used to load these data and perform operations on them such as filtering, sorting, and so on.
To write a custom estimator or transformer without Pyspark, one must use a Scala implementation. It is now easier to use mixin classes instead of scala implementations thanks to PySpark.
Advantages of PySpark:
- RDD: PySpark enables data scientists to work with Resilient Distributed Datasets more easily.
- Simple integration with other languages: The PySpark framework supports other programming languages such as Scala, Java, and R.
- Caching and disc persistence: This has a powerful caching and disc persistence mechanism for datasets, which allows it to be incredibly faster and better than others.
- Speed: This framework is known for being faster than other traditional data processing frameworks.