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Python Spark Certification Training

456+ Learners

PySpark Certification Training Course is designed to clear the CCA Spark and Hadoop Developer(CCA175) Examination. It helps to build machine learning pipelines and create ETLs. Practical learning will allow you to run much faster than Hadoop MapReduce. This course enables you to have widen knowledge about Spark MLlib, RDDs, different APIs of Spark and Spark SQL for structured processing. It covers the basic concepts of messaging systems like Kafka, data capturing using Flume and data loading using Sqoop. This certification course enables you to become a better Big Data Developer.

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Python Spark Certification

Jul 18 Sat,Sun (9 Weeks) Weekend Batch Filling Fast 02:30 PM  04:30 PM
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$ 588.00

About Course

PySpark Certification Training Course allows you to showcase the expertise level to become Big Data and Spark developers. The course is designed such a way that it covers the key concepts of PySpark Machine Learning, PySpark ecosystem and Spark APIs. StepLeaf helps you to dive into minutia of every topic in PySpark.

What will you learn in this PySpark Online Training? 

At the completion of PySpark Online Training you will be mastered in the following topics 

1. Apache Spark Architecture and its APIs 

2. Implementing tools in Spark SQL, Spark ecosystem, Kafka, Flume and Spark Streaming

3. RDD, lazy evaluation

4. Working with Dataframe using Spark SQL

5. Build various APIS with DataFrame

Who should take up this PySpark certification course? 

Python Spark is a groundbreaking technology used by many companies all around the world to process huge amounts of data in less time. This course is mainly designed for the IT professionals who work with big data and hadoop technologies.  

What are the prerequisites for this PySpark certification Training? 

There is no qualification necessary to join the PySpark training but little programming, analytical skill will help you to speed up your learning.

Why should you take up the PySpark Certification Training?

The business with Big Data has been growing at a rapid pace. PySpark is the next evolutionary change in the Big Data world which analyzes the data to leverage meaningful business insight. Furthermore, learning PySpark has increased access to Big Data and pace up with Growing Enterprise Adoption. These things will ignite you to learn PySpark more.  


Key Skills

bigdata, apachespark, pythonforspark, spark2.0architecture, functional&object-orientedmodel, sparkframework, rdds, pysparksql, dataframes, apachekafka, flume, pysparkstreaming, pysparkmachinelearning

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Course Contents

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PySpark Course Content

  • 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
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

• Sets and associated operations
• Lists and associated operations
• Tuples and associated operations
• Dictionaries and associated operations

• 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

• 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

• 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

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Structure your learning and get a certificate to prove it.

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Projects

How will I execute the practicals in this PySpark Certification Online Training?

PySpark Course case studies will be executed in StepLeaf’s Cloud Lab environment. The lab is accessed via the browser. StepLeaf instructor will be helping you in each activity. 

What is CloudLab? 

Cloud lab is designed to experiment with cloud architecture and real time PySpark Case studies. It helps you to get the deeper understanding of PySpark and its APIs. The cloud infrastructure is proactive, helps to take backup and restore data , unlimited storage capacity and automatic software integration. 

What are the system requirements for PySpark Certification Online Training? 

Since we use Cloud lab which is a pre-configured environment, we need not worry about any other system requirements. 


What are the case studies in PySpark Certification Online Training? 

Project:#1 

Domain: Financial 

Problem Statement: 

A financial institution divided its platform into various domains. It needs the view of the customer from all angles. Consolidate the data using PySpark so that the data is consolidated into a single customer file. 

Project:#2 

Domain: E-commerce 

Problem Statement: 

In the present covid situation there is high demand in online shopping for essential items. You have built a forecasting service to find which product is in demand. To address this problem, build a model and run a PySpark job to load the model and make predictions of streaming requests. 


StepLeaf PySpark course is designed to help you gain insight into the various PySpark concepts and pass the CCA Spark and Hadoop Developer Exam (CCA175). The entire course is created by industry experts to help professionals gain top positions in leading organizations. Our online training is planned and conducted according to the requirements of the certification exam.

In addition, industry-specific projects and hands-on experience with a variety of Spark tools can help you accelerate your learning. After completing the training, you will be asked to complete a quiz, which is based on the questions asked in the PySpark certification exam. Besides, we also award each candidate with Intellipaat PySpark Course Completion Certificate after he/she completes the training program along with the projects and scores the passing marks in the quiz.

Our course completion certification is recognized across the industry and many of our alumni work at leading MNCs, including Sony, IBM, Cisco, TCS, Infosys, Amazon, Standard Chartered, and more.

FAQ

StepLeaf uses a blended learning technique which consists of auditory, visual, hands-on and much more technique at the same time. We assess both students and instructors to make sure that no one falls short of the course goal. 


Yes, we offer crash courses. You could get the overview of the whole course and can drive it within a short period of time.  

Currently we don't offer demo class as the number of students who attend the live sessions are limited. You could see our recorded video of the class in each course description page to get the insight of the class and the quality of our instructors.   

StepLeaf has a study repository where you can find the recorded video of each class and all other essential resources for the course. 

Each student who joins StepLeaf will be allocated with a learning manager to whom you can contact anytime to clarify your queries 

Yes we have a centralized study repository, where students can jump in and explore all the latest materials of latest technologies. 

Assessment is a continuous process in StepLeaf where a student's goal is clearly defined and identifies the learning outcome. We conduct weekly mock tests, so that students can find their shortfalls and improve them before the final certification exam.  

StepLeaf offers a discussion board where students can react to content, share challenges, teach each other and experiment their new skills.  

You can pay your course fee online quickly through secure Razorpay gateway. You will be able to track the payment details on the way.

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