This training focuses on PySpark fundamentals, Spark architecture, DataFrame operations, transformations, actions, Spark SQL, and distributed data processing techniques.
Overview
Python & Spark Training is a practical, hands-on program designed to equip learners with the skills required to build scalable data processing applications using Python and Apache Spark. This training focuses on PySpark fundamentals, Spark architecture, DataFrame operations, transformations, actions, Spark SQL, and distributed data processing techniques. Participants will gain real-world experience in developing high-performance big data solutions for batch processing and analytics workloads.
Learning Outcomes
Participants will gain strong practical expertise in PySpark and Spark ecosystem, enabling them to process large datasets, build scalable data pipelines, and perform distributed data analytics efficiently.
Duration & Delivery Mode
16 hours
Target Audience
• Data Engineers and Data Analysts
• Python Developers entering big data domain
• Analytics Engineers and BI Professionals
• Software Developers working with data pipelines
• IT Professionals transitioning into data engineering
Pre-requisites
• Basic understanding of Python programming
• Familiarity with data structures and basic programming concepts
• Basic knowledge of SQL is helpful
• Understanding of data concepts and analytics fundamentals
Skillset Achieved
• PySpark architecture and execution model understanding
• DataFrame API and RDD operations
• Data transformation and aggregation techniques
• Spark SQL for data analysis
• Handling large-scale distributed datasets
• Building batch data processing pipelines
• Performance optimization basics in Spark
Course Outcome
Upon completion of this training, participants will be able to develop scalable data processing applications using Python and Apache Spark. They will be capable of building efficient ETL pipelines, performing large-scale data analysis, and optimizing distributed data workflows.
Course Outline
Introduction to Python and Spark Ecosystem
• Overview of Apache Spark architecture
• PySpark setup and environment configuration
• RDD concepts and distributed computing basics
• Creating and managing Spark sessions
PySpark DataFrame Fundamentals
• Creating DataFrames from different data sources
• Data selection, filtering, and transformation
• Basic aggregations and grouping operations
• Handling missing and structured data
Spark SQL and Advanced Data Processing
• Introduction to Spark SQL
• Writing SQL queries on DataFrames
• Joins and complex transformations
• Window functions overview
Performance and Real-World Data Pipelines
• Introduction to Spark optimization concepts
• Caching and persistence strategies
• Batch data pipeline development
• Best practices for PySpark applications
Assessment Topics
• Spark architecture and execution model
• PySpark DataFrames and RDDs
• Data transformations and aggregations
• Spark SQL and query processing
• Distributed data processing concepts
• Basic performance optimization
Evaluation
• Hands-on PySpark coding exercises
• Data transformation and analysis tasks
• Spark SQL query assignments
• Mini project on batch data processing pipeline
Course Materials
Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.
Certification
Participants who successfully complete the training will receive an AcadNXT Certification in Python & Spark Training, validating their expertise in PySpark development, distributed data processing, Spark SQL, and big data analytics using Apache Spark.
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What Our Students Say
“The training made PySpark concepts very easy to understand with practical examples.”
“Excellent hands-on sessions covering Spark DataFrames and SQL operations.”
“The course helped me build confidence in working with large datasets.”
“Very structured training with strong focus on real-world Spark applications.”
“This course is perfect for learning Python-based big data processing.”