PySpark Training

Python is the most popular interpreted and object-oriented programming language. Python syntax is very easy compared to other programming languages. Python is used everywhere in the market because it is very easy to code in Python. Many fields like Data Science, Machine Learning, Artificial Intelligence are using Python Programming to easier their ways to code to make machines understand human-understandable codes.

Topics Covered in this module are:

• Python variables
• Built-in functions
• Expressions
• Looping statements
• Keywords and operators
• Python exceptions
• Control Statements
• Strings
• Lists and Tuples

Big Data is an intermediate field that is mainly used to analyze data and extract information from the large volume data sets. In this module, you will get a basic idea of all the fundamental concepts of Big Data.

Topics Covered in this module are:

• Overview of Big Data Analytics
• Big Data Life Cycle
• Cleansing Data
• Data Visualization
• Data Tools
• Statistical Methods
• Logistic Regression

Apache Spark engine has the capability to work with huge data sets by processing them parallel in the form of back systems. Spark is also used for Machine Learning and large scale distributed data processing. Apache Spark is a cluster-computing framework that is mainly used to handle big data analysis. Moreover, It also provides an interface for programming entire clusters along with data parallelism.

Topics Covered in this module are:

• What is Apache Spark
• Evolution of Apache Spark
• Key features of Apache Spark
• Key components of Apache Spark
• Apache Spark Installation
• Advanced Spark Programming

Apache Spark follows the master-slave architecture that mainly consists of one master and a number of slaves. The architecture depends on both abstractions one is Resilient Distributed Dataset (RDD) and the other is Directed Acyclic Graph (DAG). Apache Spark is a unified computing engine that is mainly used to handle big data analysis.

Topics Covered in this module are:

• The workflow in Spark Architecture
• What is Resilient Distributed Dataset 
• What is DAG
• Key components of Apache Spark
• Understanding Spark SQL, Spark Core, and Spark Streaming.

Spark RDD is abbreviated as Spark Resilient Distributed Dataset. It is considered as the core abstraction of Spark. RDD is defined as a collection of elements that are partitioned across the cluster nodes to provide ways to execute various parallel operations. 

Topics Covered in this module are:

• What is Spark RDD
• Various RDD operations
• RDD shared variables
• RDD persistence Ways to create RDDs
• Use of external datasets

• What is Spark SQL?
• Why spark SQL?
• Overview of Spark SQL Architecture
• Exploring the SQLContext in Spark SQL
• Defining Schema RDDs
• Understanding User Defined Functions
• Exploring different Data Frames & Datasets
• Learn on how to interoperate with RDDs
• JSON and Parquet File Formats
• Explore to load Data through Different Sources
• Understanding about the Spark-Hive Integration 

In this module, you will go through the various built-in functions that are available in Apache Spark.

Topics Covered in this module are:

• Cartesian Function 
• Union Function
• Filter Function
• Co-Group Function
• Intersection Function
• Count Function
• Map Function
• reduced
• ByKey Function
• Distinct Function 

PySpark is a Python API that is mainly designed to support Apache Spark. In PySpark, an API is written in Python programming to provide enhanced support for the Spark computational engine.

Topics Covered in this module are:

• Introduction to Python Programming
• What is Apache Spark
• Python with Apache Spark
• Need for Python in Apache Spark
• PySpark Environment Setup
• PySpark Storage levels
• Basic fundamentals of Python Programming 

A Machine Learning API is offered to Apache Spark i.e., named as PySpark MLlib. It also supports different kinds of algorithms like MLlib.classification, MLlib.fpm, and many more. 

Topics Covered in this module are:

• Introduction to Machine Learning
• What are the datasets used
• Algorithms Machine Learning API
• Random Forest
• What is a Decision tree
• Naive, Bayes 

• Understanding about the Supervised Learning patterns such as Linear Regression, Logistic Regression, Decision Tree, Random Forest
• Exploring the Unsupervised Learning patterns such as K-Means Clustering & How It Works with MLlib 
• Evaluating US Election Data using MLlib (K-Means). 

Serialization in Apache Spark is mainly used for performing performance tuning. This technique plays a major role in performing costly operations. Serializers are supported in PySpark for performance tuning.

Topics Covered in this module are:

• What is Serialization
• Different types of Serializers supported by PySpark.
• What is performance tuning 

• Introduction to kafka
• Why kafka?
• Explore the core Concepts related to Kafka 
• Understanding the Kafka Architecture and its implementation.
• Understanding the Components of Kafka Cluster
• Learn on how to Configure Kafka Cluster
• Learn about the Kafka Producer and Consumer Java API
• Why is Apache Flume needed?
• What is Apache Flume
• Flume Architecture overview
• Explore the Flume Sources,Flume Sinks, and Flume Channels
• Understanding the Flume Configuration
• Integrating Apache Flume and Apache Kafka 

• Need for streaming
• Overview of Spark Streaming
• Spark Streaming Features
• Understanding the Spark Streaming Workflow
• How Uber Uses Streaming Data 
• Exploring the Streaming Context & DStreams and transformations on DStreams
• Describe Windowed Operators and Why it is Useful
• Explore the vital Windowed Operators
• Understanding about the Slice, Window and ReduceBy Window Operators and stateful Operators 

• Overview of Streaming Data Source 
• Apache Flume and Apache Kafka Data Sources 

• Overview of Spark GraphX
• Explore the data related to a Graph
• Understanding the GraphX Basic APIs and Operations
• Exploring different Spark GraphX Algorithms such as: PageRank, Personalized PageRank, Triangle Count, Shortest Paths, Connected Components, Strongly Connected Components, Label Propagation. 

• Summarize all the points discussed. 

At HKR, we provide practice tests at the end of the course , frequently asked interview questions and answers, community questions and answers, mock interview sessions, and Sample Resumes to crack the interview. 

HKR Trainings provides PySpark Interview Questions, PySpark Tutorial, PySpark Sample Resume and PySpark Community for clear understanding.