PySpark Course And Certification
What is PySpark?
PySpark is the collaboration of Apache Spark and Python.
Apache Spark is known as an open-source cluster-computing framework, built with speed, it's simple in usage and streaming analytics.
Python is a general purpose, high-level programming language.
PySpark is a Python API written in python to give support to Apache Spark. Apache Spark is written in Scala and may also be integrated with Python, Java, Scala, R, SQL languages.
Spark is essentially a computational engine, that works with huge sets of knowledge by processing them in parallel and batch systems.
PySpark is a great language for used for exploratory data analysis at scale, in building machine learning pipelines, and also creating ETLs
More about PySpark: It is a Python API for Spark that is released by the Apache Spark community that gives support to Python with Spark. Making use of PySpark, one can also easily integrate and work with RDDs in Python Programming language too. There are different features that make PySpark such an amazing framework when it comes to working with huge datasets. Either it is to perform computations on large datasets or to just analyze them, Data Engineers are now switching to this great tool.
Features Of PySpark
Some Key Features of PySpark
Real-time Computations: Just because of the in-memory processing in the PySpark framework, it shows low latency.
Polyglot: The PySpark framework is very compatible with different languages such as the Scala, Java, Python, and R, which makes Pyspark one of the most used and preferable frameworks for processing huge datasets.
Caching and Disk Persistence: This framework provides very powerful caching and great disk persistence.
Fast Processing: The PySpark framework is a very fast framework, way faster than other traditional frameworks for Big Data processing.
Pyspark works very well with RDDs: It is noted that Python programming language is dynamically typed, it helps when working with RDDs.
Extraction: Extracting features from “raw” data
Transformation: Scaling, converting, or modifying features
Selection: Selecting a subset from a larger set of features
Locality Sensitive Hashing (LSH): This class of algorithms combines aspects of feature transformation with other algorithms.
Benefits Of PySpark
1. Dynamic in Nature: Being dynamic in nature, it helps you to develop a parallel application, as Spark provides 80 high-level operators.
2. Fault Tolerance in Spark: Through Spark abstraction-RDD, PySpark provides fault tolerance. The programming language is specifically designed to handle the malfunction of any worker node in the cluster, ensuring that the loss of data is reduced to zero.
3. Real-Time Stream Processing: PySpark is renowned and much better than other languages when it comes to real-time stream processing.
Earlier the problem with Hadoop MapReduce was that it can manage data which is already present, but it cannot manage the real-time data. However, with PySpark Streaming, this problem is reduced significantly.
Why Study PySpark?
Let's look at the need for PySpark
1. PySpark gives more solutions to deal with big data better, especially if you have to switch between tools to perform different types of operations on big data.
2. PySpark is one of those amazing tools that help handle big data in Apache Spark.
3. Increase your earning potential with PySpark skills and certification.
4. Job opportunities and career advancement.
5. Enrich your CV and attract better position.
PySpark Course Outline:
PySpark - Introduction
PySpark - Environment Setup
PySpark - SparkContext
PySpark - RDD
PySpark - Broadcast & Accumulator
PySpark - SparkConf
PySpark - SparkFiles
PySpark - StorageLevel
PySpark - MLlib
PySpark - Serializers
PySpark - Video Lectures
PySpark - Exams and Certification