APACHE PYSPARK

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The Apache PySpark Syllabus focuses on big data processing using Python with the Apache Spark framework. It covers distributed data processing, data transformations, Spark SQL, DataFrames, performance optimization, and real-time data handling. This training explains how PySpark enables fast and scalable data processing across large datasets. It is important because organizations rely on big data analytics for insights, decision-making, and data-driven strategies. It is ideal for data engineers, data analysts, data scientists, and IT professionals who want to work with large-scale data processing and analytics systems.

Level: Beginner → Advanced
🎯 Focus: Big Data Processing | Data Engineering | ETL | Real-Time Analytics
Duration of Training : 32 – 40 Hours
Batch type : Weekdays/Weekends
Mode of Training : Classroom/Online/Corporate Training
Detailed Syllabus • Hands-on Labs • Assignments • Support-Focused • Implementation

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Overview

The Syllabus

Curriculum Designed by Experts

Module 1: Big Data & Pyspark Fundamentals

Duration: 4 Hours
Topics:
• Big Data concepts (Volume, Velocity, Variety)
• Hadoop Ecosystem overview
• What is Apache Spark?
• Spark vs Hadoop (MapReduce)
• Spark Architecture (Driver, Executor, Cluster Manager)
• Introduction to PySpark
🧪 Assignments:
• Install Spark (Local Mode)
• Run first PySpark program
🚀 Mini Task:
👉 Word count using PySpark

Module 2: Rdd (resilient Distributed Dataset)

Duration: 6 Hours
Topics:
• RDD basics
• Transformations vs Actions
• Lazy evaluation
• RDD operations (map, filter, reduce, flatMap)
• Persistence & caching
🧪 Assignments:
• Perform transformations on dataset
• Count words from large log file
🚀 Mini Project:
👉 Log processing using RDD

Module 3: Dataframes & Spark Sql

Duration: 8 Hours
Topics:
• DataFrames vs RDD
• Schema & Data Types
• Spark SQL
• Reading data (CSV, JSON, Parquet)
• Writing data to storage
🧪 Assignments:
• Load CSV and perform transformations
• Convert RDD to DataFrame
🚀 Project:
👉 Sales data analysis using DataFrames

Module 4: Data Processing & Transformations

Duration: 6 Hours
Topics:
• Filtering, grouping, aggregation
• Joins (inner, outer, left, right)
• Window functions
• Handling null values
🧪 Assignments:
• Perform joins on multiple datasets
• Aggregate large dataset
🚀 Mini Project:
👉 Customer analytics system

Module 5: Pyspark With Files & Storage Systems

Duration: 5 Hours
Topics:
• HDFS basics
• Working with S3 / Azure Data Lake
• File formats (Parquet, ORC, Avro)
• Partitioning & bucketing
🧪 Assignments:
• Read/write data from S3
• Convert CSV to Parquet
💼 Scenario:
👉 Optimize storage for large-scale data processing

Module 6: Performance Tuning In Pyspark

Duration: 5 Hours
Topics:
• Partitioning strategies
• Caching & persistence
• Broadcast joins
• Catalyst optimizer
• Shuffle operations
🧪 Assignments:
• Optimize slow queries
• Reduce shuffle operations
💼 Scenario:
👉 Improve job performance for large dataset

Module 7: Pyspark With AWS/ Azure

Duration: 5 Hours
Topics:
• Running PySpark on AWS EMR / Databricks
• Azure Databricks basics
• Cluster setup
🧪 Assignments:
• Run PySpark job on cloud cluster
• Load data from cloud storage
🚀 Project:
👉 Cloud-based ETL pipeline

Module 8: Pyspark Streaming (real-time Data)

Duration: 5 Hours
Topics:
• Structured Streaming
• Streaming sources (Kafka basics)
• Window operations in streaming
• Real-time data processing
🧪 Assignments:
• Process streaming data
• Perform real-time aggregation
🚀 Project:
👉 Real-time log monitoring system

Module 9: Pyspark With Etl Pipelines

Duration: 4 Hours
Topics:
• ETL pipeline design
• Data cleaning & transformation
• Scheduling jobs (Airflow basics optional)
🧪 Assignments:
• Build ETL pipeline
💼 Scenario:
👉 Daily batch processing system

Module 10: Debugging & Troubleshooting

Duration: 3 Hours
Topics:
• Debugging Spark jobs
• Handling failures
• Log analysis
🧪 Assignments:
• Fix failed Spark job
💼 Scenario:
👉 Production job failure handling

🔥 Final Capstone Projects

🥇 Project 1: E-Commerce Data Pipeline
• Process sales data
• Generate insights
• Store in data warehouse
🥈 Project 2: Real-Time Log Analytics
• Stream logs using Kafka
• Analyze using PySpark
🥉 Project 3: Cloud ETL Pipeline
• Extract data from S3
• Transform using PySpark
• Load into target system
🏅 Project 4: Customer Recommendation Engine
• Analyze user behavior
• Generate recommendations

💼 50+ Real-time Job Scenarios

• Handling TB-level data processing
• Optimizing slow Spark jobs
• Debugging failed ETL pipelines
• Designing scalable data pipelines
• Managing cloud-based big data workloads

📁 Deliverables

• ✅ 4+ real-time projects
• ✅ ETL pipelines
• ✅ Performance tuning scripts
• ✅ Resume-ready use cases

🎯 Outcome

After this training, candidates can:
• Work as Data Engineer / Big Data Engineer
• Build scalable ETL pipelines
• Handle real-time streaming data
• Optimize Spark jobs

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Apache Pyspark Course Certification With Training In Kochi

Q: Module 1: Big Data & Pyspark Fundamentals

Duration: 4 Hours Topics: • Big Data concepts (Volume, Velocity, Variety) • Hadoop Ecosystem overview • What is Apache Spark? • Spark vs Hadoop (MapReduce) • Spark Architecture (Driver, Executor, Cluster Manager) • Introduction to PySpark 🧪 Assignments: • Install Spark (Local Mode) • Run first PySpark program 🚀 Mini Task: 👉 Word count using PySpark

Q: Module 2: Rdd (resilient Distributed Dataset)

Duration: 6 Hours Topics: • RDD basics • Transformations vs Actions • Lazy evaluation • RDD operations (map, filter, reduce, flatMap) • Persistence & caching 🧪 Assignments: • Perform transformations on dataset • Count words from large log file 🚀 Mini Project: 👉 Log processing using RDD

Q: Module 3: Dataframes & Spark Sql

Duration: 8 Hours Topics: • DataFrames vs RDD • Schema & Data Types • Spark SQL • Reading data (CSV, JSON, Parquet) • Writing data to storage 🧪 Assignments: • Load CSV and perform transformations • Convert RDD to DataFrame 🚀 Project: 👉 Sales data analysis using DataFrames

Q: Module 4: Data Processing & Transformations

Duration: 6 Hours Topics: • Filtering, grouping, aggregation • Joins (inner, outer, left, right) • Window functions • Handling null values 🧪 Assignments: • Perform joins on multiple datasets • Aggregate large dataset 🚀 Mini Project: 👉 Customer analytics system

Q: Module 5: Pyspark With Files & Storage Systems

Duration: 5 Hours Topics: • HDFS basics • Working with S3 / Azure Data Lake • File formats (Parquet, ORC, Avro) • Partitioning & bucketing 🧪 Assignments: • Read/write data from S3 • Convert CSV to Parquet 💼 Scenario: 👉 Optimize storage for large-scale data processing

Q: Module 6: Performance Tuning In Pyspark

Duration: 5 Hours Topics: • Partitioning strategies • Caching & persistence • Broadcast joins • Catalyst optimizer • Shuffle operations 🧪 Assignments: • Optimize slow queries • Reduce shuffle operations 💼 Scenario: 👉 Improve job performance for large dataset

Q: Module 7: Pyspark With AWS/ Azure

Duration: 5 Hours Topics: • Running PySpark on AWS EMR / Databricks • Azure Databricks basics • Cluster setup 🧪 Assignments: • Run PySpark job on cloud cluster • Load data from cloud storage 🚀 Project: 👉 Cloud-based ETL pipeline

Q: Module 8: Pyspark Streaming (real-time Data)

Duration: 5 Hours Topics: • Structured Streaming • Streaming sources (Kafka basics) • Window operations in streaming • Real-time data processing 🧪 Assignments: • Process streaming data • Perform real-time aggregation 🚀 Project: 👉 Real-time log monitoring system

Q: Module 9: Pyspark With Etl Pipelines

Duration: 4 Hours Topics: • ETL pipeline design • Data cleaning & transformation • Scheduling jobs (Airflow basics optional) 🧪 Assignments: • Build ETL pipeline 💼 Scenario: 👉 Daily batch processing system

Q: Module 10: Debugging & Troubleshooting

Duration: 3 Hours Topics: • Debugging Spark jobs • Handling failures • Log analysis 🧪 Assignments: • Fix failed Spark job 💼 Scenario: 👉 Production job failure handling

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