Pyspark Training Course

When you enroll in our PySpark certification course and complete the training program, you will:

• Become familiar with Apache Spark, its applicability and Spark 2.0 architecture
• Gain hands-on expertise with the various tools in the Spark ecosystem, including Spark MLlib, Spark SQL, Kafka, Flume and Spark Streaming
• Understand the architecture of RDD, lazy evaluation, etc.
• Learn how to change the architecture of the DataFrame and how to interact with it using Spark SQL
• Build various APIs that work with Spark DataFrame
• Pick up the skills to aggregate, filter, sort and transform data using DataFrame

Big Data analytics is experiencing constant growth, thus, providing an excellent opportunity for all IT kinds of IT/ITES professionals. Thus, learning PySpark is an outstanding career transition. Further, professional hailing from the following domains can enroll in our PySpark course:

• Software developers and architects
• ETL and DW professionals
• BI experts
• Senior IT expert
• Mainframe developers
• Data Science engineers
• Big data engineers, developers, and architects, etc.

We do not enforce any prerequisite for enrolling in our PySpark online training. However, basic programming skills can help you speed up your learning. However, you can still join our PySpark Certification Program without any extensive programming experience. Our online real-time training is conducted by industry experts, and under their guidance, you can easily pick up the basics of any topic/domain.

• In the US, Data Spark Developer has an average annual salary of $150,000 – Neuvoo
• The average salary range for “Apache Spark Developers” is from US$92,176 a year for the developer to $126,114 a year for back-end developers. – Indeed
• Big data market revenue is expected to grow from $42 billion (2018) to $103 billion in 2027! – Forbes
• 79% of company executives say that companies that do not embrace Big Data are losing market control and may become non-existent – Accenture

Almost all the companies that rely on Big Data, use Spark as part of their solution strategy. Therefore, the job requirements in either Big Data or PySpark is not going to reduce in the upcoming years. So, “now,” is the perfect time to upskill your PySpark learning and enroll yourself in a recognized PySpark training course.

• Explaining Python and Highlighting Its Importance
• Setting up Python Environment and Discussing Flow Control
• Running Python Scripts and Exploring Python Editors and IDEs

• Defining Reserve Keywords and Command Line Arguments
• Describing Flow Control and Sequencing
• Indexing and Slicing
• Learning the xrange() Function
• Working Around Dictionaries and Sets
• Working with Files

• Explaining Functions and Various Forms of Function Arguments
• Learning Variable Scope, Function Parameters, and Lambda Functions
• Sorting Using Python
• Exception Handling
• Package Installation
• Regular Expressions

• Using Class, Objects, and Attributes
• Developing Applications Based on OOP
• Learning About Classes, Objects and How They Function Together
• Explaining OOPs Concepts Including Inheritance, Encapsulation, and Polymorphism, Among Others

• Debugging Python Scripts Using pdb and IDE
• Classifying Errors and Developing Test Units
• Implementing Databases Using SQLite
• Performing CRUD Operations

• What is Big Data?
• 5 V’s of Big Data
• Problems related to Big Data: Use Case
• What tools available for handling Big Data?
• What is Hadoop?
• Why do we need Hadoop?
• Key Characteristics of Hadoop
• Important Hadoop ecosystem concepts
• MapReduce and HDFS
• Introduction to Apache Spark
• What is Apache Spark?
• Why do we need Apache Spark?
• Who uses Spark in the industry?
• Apache Spark architecture
• Spark Vs. Hadoop
• Various Big data applications using Apache Spark

• Introduction to PySpark
• Who uses PySpark?
• Why Python for Spark?
• Values, Types, Variables
• Operands and Expressions
• Conditional Statements
• Loops
• Numbers
• Python files I/O Functions
• Strings and associated operations
• Sets and associated operations
• Lists and associated operations
• Tuples and associated operations
• Dictionaries and associated operations

Hands-On:

• Demonstrating Loops and Conditional Statements
• Tuple – related operations, properties, list, etc.
• List – operations, related properties
• Set – properties, associated operations
• Dictionary – operations, related properties

• Functions
• Lambda Functions
• Global Variables, its Scope, and Returning Values
• Standard Libraries
• Object-Oriented Concepts
• Modules Used in Python
• The Import Statements
• Module Search Path
• Package Installation Ways

Hands-On:

• Lambda – Features, Options, Syntax, Compared with the Functions
• Functions – Syntax, Return Values, Arguments, and Keyword Arguments
• Errors and Exceptions – Issue Types, Remediation
• Packages and Modules – Import Options, Modules, sys Path

• Spark Components & its Architecture
• Spark Deployment Modes
• Spark Web UI
• Introduction to PySpark Shell
• Submitting PySpark Job
• Writing your first PySpark Job Using Jupyter Notebook
• What is Spark RDDs?
• Stopgaps in existing computing methodologies
• How RDD solve the problem?
• What are the ways to create RDD in PySpark?
• RDD persistence and caching
• General operations: Transformation, Actions, and Functions
• Concept of Key-Value pair in RDDs
• Other pair, two pair RDDs
• RDD Lineage
• RDD Persistence
• WordCount Program Using RDD Concepts
• RDD Partitioning & How it Helps Achieve Parallelization
• Passing Functions to Spark

Hands-On:

• Building and Running Spark Application
• Spark Application Web UI
• Loading data in RDDs
• Saving data through RDDs
• RDD Transformations
• RDD Actions and Functions
• RDD Partitions
• WordCount program using RDD’s in Python

• Need for Spark SQL
• What is Spark SQL
• Spark SQL Architecture
• SQL Context in Spark SQL
• User-Defined Functions
• Data Frames
• Interoperating with RDDs
• Loading Data through Different Sources
• Performance Tuning
• Spark-Hive Integration

Hands-On:

• Spark SQL – Creating data frames
• Loading and transforming data through different sources
• Spark-Hive Integration

• Why Kafka
• What is Kafka?
• Kafka Workflow
• Kafka Architecture
• Kafka Cluster Configuring
• Kafka Monitoring tools
• Basic operations
• What is Apache Flume?
• Integrating Apache Flume and Apache Kafka

Hands-On:

• Single Broker Kafka Cluster
• Multi-Broker Kafka Cluster
• Topic Operations
• Integrating Apache Flume and Apache Kafka

• Introduction to Spark Streaming
• Features of Spark Streaming
• Spark Streaming Workflow
• StreamingContext Initializing
• Discretized Streams (DStreams)
• Input DStreams, Receivers
• Transformations on DStreams
• DStreams Output Operations
• Describe Windowed Operators and Why it is Useful
• Stateful Operators
• Vital Windowed Operators
• Twitter Sentiment Analysis
• Streaming using Netcat server
• WordCount program using Kafka-Spark Streaming

Hands-On:

• Twitter Sentiment Analysis
• Streaming using Netcat server
• WordCount program using Kafka-Spark Streaming
• Spark-flume Integration

• Introduction to Machine Learning- What, Why and Where?
• Use Case
• Types of Machine Learning Techniques
• Why use Machine Learning for Spark?
• Applications of Machine Learning (general)
• Applications of Machine Learning with Spark
• Introduction to MLlib
• Features of MLlib and MLlib Tools
• Various ML algorithms supported by MLlib
• Supervised Learning Algorithms
• Unsupervised Learning Algorithms
• ML workflow utilities

Hands-On:

• K- Means Clustering
• Linear Regression
• Logistic Regression
• Decision Tree
• Random Forest