PySpark Training in Pune

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PrwaTech PySpark certification Course is aimed at providing knowledge to the learner about Distributed Data Processing using Apache Spark and Python. The course includes fundamental concepts of Big Data, Spark architecture, data processing, Machine Learning workflows and implementing real time Analyst.





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    • ✔ Lifetime access to high-quality self-paced e-learning content curated by industry experts
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    ₹5000 ₹10000

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    ₹20500 ₹25500

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    ₹22000 ₹27000

    Master Big Data Processing with PySpark Training at PrwaTech

    In today’s data-driven world, organizations generate enormous volumes of structured and unstructured data every day. Managing, processing, and analyzing this data efficiently requires modern Big Data technologies and distributed computing frameworks. PySpark is one of the most widely used technologies for Big Data Processing, Data Engineering, Machine Learning, and Real-Time Analytics.   PrwaTech offers a complete and industry-oriented PySpark Training in Pune designed for students, software developers, data engineers, Big Data professionals, and analytics enthusiasts who want to build successful careers in Big Data and distributed computing technologies.   Our PySpark training program emphasizes hands-on learning, real-world Big Data applications, and practical implementation to help students gain expertise in distributed data processing, Spark architecture, data transformation, Machine Learning integration, and scalable analytics solutions.   PrwaTech has years of experience delivering advanced technical training in Hadoop, Big Data, Data Science, Artificial Intelligence, and cloud technologies. We have trained professionals from leading organizations including IBM, HCL, Wipro, Accenture, ITC, and Crisil.   Whether you are a beginner exploring Big Data technologies or a working professional upgrading your technical expertise, our PySpark course provides structured learning from fundamentals to advanced implementation using Apache Spark.  

    Course Overview

    The PySpark Training Course at PrwaTech is designed to provide learners with practical and theoretical knowledge of distributed data processing using Apache Spark and Python.   The course covers Big Data fundamentals, Spark architecture, large-scale data processing, Machine Learning workflows, and real-time analytics implementation.   Students gain practical experience in managing large datasets, executing distributed computing tasks, building scalable data pipelines, and implementing enterprise-level Big Data solutions.  

    Tools and Technologies Covered

    • Apache Spark
    • PySpark
    • Hadoop Ecosystem
    • Spark SQL
    • Spark DataFrames
    • Spark Streaming
    • Python Programming
    • Hive
    • HDFS (Hadoop Distributed File System)
    • Kafka Basics
    • Machine Learning with MLlib
    • Jupyter Notebook
    • Data Processing and Transformation
    • Real-Time Analytics

    Practical Learning with Real-Time Projects

    At PrwaTech, practical implementation and project-based learning are essential for mastering PySpark. Students work on real-world scenarios and enterprise-level Big Data environments.  

    Students Will Work On:

    • Distributed Data Processing Projects
    • Data Transformation and Data Cleaning
    • Spark SQL Implementation
    • Real-Time Analytics Workflows
    • Machine Learning Integration with Spark
    • ETL Pipeline Development
    • Big Data Processing Use Cases
    • Streaming and Batch Processing Projects

    Why Learn PySpark?

    • High demand for Big Data professionals worldwide
    • Develop expertise in distributed computing
    • Build scalable data engineering skills
    • Access excellent career growth opportunities
    • Learn enterprise-level Big Data technologies
    • Gain practical implementation experience

    What Will Students Learn?

    • PySpark Fundamentals
    • Apache Spark Architecture
    • Spark DataFrames and RDDs
    • Spark SQL and Query Processing
    • Distributed Data Processing
    • Data Cleaning and Transformation
    • Real-Time Data Streaming
    • Hadoop and HDFS Integration
    • Machine Learning using Spark MLlib
    • ETL Pipeline Development
    • Big Data Analytics Workflows
    • Real-Time Project Implementation

    Skills Gained

    • Big Data Processing Skills
    • PySpark Development Expertise
    • Distributed Computing Knowledge
    • Spark SQL and DataFrame Expertise
    • Hadoop Ecosystem Understanding
    • Real-Time Data Analytics Skills
    • ETL Pipeline Development
    • Machine Learning Integration Skills
    • Data Transformation and Processing Skills
    • Python Programming Expertise
    • Problem-Solving and Analytical Thinking
    • Real-Time Project Experience

    Why Choose PrwaTech?

    • Trusted PySpark Training Institute in Pune
    • Experienced Industry Trainers
    • Hands-On Practical Learning
    • Live Big Data Projects and Case Studies
    • Updated Industry-Oriented Curriculum
    • Flexible Online and Classroom Training
    • Certification Guidance and Support
    • Placement and Career Assistance
    • Personalized Mentorship Sessions

    Career Opportunities After PySpark Training

    • PySpark Developer
    • Big Data Engineer
    • Data Engineer
    • Hadoop Developer
    • Spark Developer
    • ETL Developer
    • Data Analyst
    • Machine Learning Engineer

    Enroll Today

    Start your Big Data journey with PrwaTech. Learn from industry experts, gain hands-on experience with PySpark and Apache Spark technologies, and build real-world projects that prepare you for enterprise environments.   Enroll in the Best PySpark Training Institute in Pune and become a future-ready Big Data professional with practical expertise and industry-focused knowledge.
    Module 1 : Big Data & Spark Fundamentals
    • Evolution of Big Data
    • Challenges of Traditional Systems
    • Hadoop Ecosystem Overview
    • Introduction to Apache Spark
    • Spark vs Hadoop MapReduce
    • Spark Core Components
    • Cluster Managers (Standalone, YARN, Mesos)
    • Spark Architecture & Execution Flow
    • Driver, Executor & Worker Nodes
    • DAG & Lazy Evaluation
    • Spark Deployment Modes
    Module 2 : Python Essentials for PySpark
    • Python Basics & Syntax
    • Functions & Lambda Expressions
    • OOPs Concepts
    • Collections & Iterators
    • Exception Handling
    • File Handling
    • Virtual Environments
    • Logging in Python
    Module 3 : Linux & Git for Data Engineers
    • Linux Commands for Data Engineering
    • File Permissions
    • Shell Scripting Basics
    • Environment Variables
    • Git Fundamentals
    • Git Branching & Merge
    • GitHub Workflow
    Module 4 : Spark RDD Programming
    • RDD Fundamentals
    • Creating RDDs
    • Parallelization
    • Transformations & Actions
    • Narrow vs Wide Transformations
    • Pair RDD Operations
    • Persistence & Caching
    • Shared Variables (Broadcast & Accumulator)
    Module 5 : DataFrames & Spark SQL
    • Introduction to DataFrames
    • Schema Inference
    • DataFrame Operations
    • Spark SQL Architecture
    • Temporary & Global Views
    • Complex Queries
    • UDFs (User Defined Functions)
    Module 6 : Data Processing & ETL Pipelines
    • ETL Concepts
    • Data Cleansing Techniques
    • Handling Missing Data
    • Deduplication
    • Incremental Loads
    • CDC (Change Data Capture)
    • Data Validation
    • Pipeline Design Patterns
    • Error Handling & Retry Mechanism
    Module 7 : Working with File Formats & Storage
    • CSV, JSON, Avro, Parquet, ORC
    • Compression Techniques
    • HDFS Fundamentals
    • Partitioning & Bucketing
    • Reading/Writing Optimized Files
    • S3 Integration
    • Azure Blob Integration
    Module 8 : Advanced Spark Transformations
    • Joins & Join Strategies
    • Broadcast Joins
    • Aggregations
    • Pivot & Unpivot
    • Window Analytics
    • Complex Transformations
    • Data Skew Handling
    Module 9 : Spark Performance Optimization
    • Spark UI
    • Query Execution Plan
    • Catalyst Optimizer
    • Tungsten Engine
    • Partition Optimization
    • Shuffle Optimization
    • Memory Management
    • Caching Strategies
    • Performance Tuning Best Practices
    • Analyze DAG Execution
    Module 10 : PySpark with Cloud & Databricks
    • Introduction to Databricks
    • Databricks Workspace
    • Notebooks & Clusters
    • AWS EMR
    • AWS Glue Basics
    • Azure Databricks
    • Job Scheduling
    Module 11 : Real-Time Streaming with Spark
    • Structured Streaming
    • Streaming Architecture
    • Kafka Basics
    • Kafka Integration with Spark
    • Real-Time ETL
    • Watermarking
    • Checkpointing
    Module 12 : Delta Lake & Lakehouse Concepts
    • Data Lake vs Data Warehouse
    • Lakehouse Architecture
    • Delta Lake Features
    • ACID Transactions
    • Time Travel
    • Merge & Upsert
    Module 13 : Workflow Orchestration & Automation
    • Apache Airflow Basics
    • DAG Creation
    • Scheduling Pipelines
    • Monitoring Jobs
    • Alerts & Notifications
    Module 14 : Capstone Project (Industry Project)
    • Retail Analytics
    • Banking Transactions
    • Telecom Logs
    • Healthcare Data
    • Project Flow
    About Course

    PySpark is a powerful big data processing framework built on top of Apache Spark using Python. It is widely used for large-scale data processing, data engineering, machine learning, and analytics applications across industries such as banking, healthcare, e-commerce, and telecommunications. This course helps learners understand distributed computing concepts and teaches them how to process massive datasets efficiently using PySpark.

    Students will learn core topics such as RDDs, DataFrames, Spark SQL, transformations, actions, joins, window functions, and real-time streaming. The training includes hands-on practical sessions, industry-based projects, and performance optimization techniques to provide real-world experience and industry-ready skills.

    The course is suitable for beginners, software developers, data analysts, and aspiring data engineers who want to build expertise in big data technologies. Learners will also gain knowledge of integrating PySpark with cloud platforms and modern data ecosystems. By the end of the course, participants will be able to develop scalable big data applications and efficient ETL pipelines using PySpark.

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