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.

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.

• 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

• 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

• 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

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.

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

• 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

• Hands-on PySpark coding exercises
• Data transformation and analysis tasks
• Spark SQL query assignments
• Mini project on batch data processing pipeline

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

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.