Apache Spark Developer Certification

Module 1: Introduction to Apache Spark

• Big Data Fundamentals
• Apache Spark Overview
• Spark Ecosystem
• Spark Use Cases
• Industry Applications

Module 2: Spark Architecture

• Driver Program
• Executors
• Cluster Manager
• Spark Components
• Distributed Computing Concepts

Module 3: Installing and Configuring Spark

• Environment Setup
• Spark Installation
• Configuration Files
• Cluster Setup
• Development Environment

Module 4: Spark Core

• Spark Core Concepts
• Job Execution
• Tasks and Stages
• DAG Execution
• Fault Tolerance

Module 5: Resilient Distributed Datasets (RDDs)

• RDD Fundamentals
• Creating RDDs
• Transformations
• Actions
• Persistence

Module 6: DataFrames

• DataFrame Architecture
• Creating DataFrames
• DataFrame Operations
• Schema Management
• Data Processing

Module 7: Spark SQL

• SQL Queries
• Structured Data Processing
• Temporary Views
• Catalog Management
• SQL Optimization

Module 8: Data Processing Techniques

• Filtering Data
• Aggregations
• Joins
• Sorting
• Window Functions

Module 9: Spark Streaming

• Streaming Fundamentals
• Structured Streaming
• Real-Time Data Processing
• Streaming Sources
• Streaming Sinks

Module 10: Machine Learning with MLlib

• MLlib Overview
• Data Preparation
• Feature Engineering
• Model Training
• Model Evaluation

Module 11: Graph Processing

• Graph Concepts
• Graph Processing Libraries
• Graph Analytics
• Graph Algorithms
• Graph Applications

Module 12: File Formats and Data Sources

• CSV Files
• JSON Files
• Parquet Files
• ORC Files
• External Data Sources

Module 13: Performance Optimization

• Caching
• Partitioning
• Resource Optimization
• Query Optimization
• Performance Tuning

Module 14: Spark Cluster Management

• Standalone Cluster
• YARN Integration
• Kubernetes Integration
• Cluster Monitoring
• Resource Allocation

Module 15: Spark Security

• Authentication
• Authorization
• Data Encryption
• Access Control
• Security Best Practices

Module 16: Monitoring and Debugging

• Spark UI
• Log Analysis
• Job Monitoring
• Debugging Techniques
• Error Resolution

Module 17: Integration with Big Data Ecosystem

• Hadoop Integration
• Hive Integration
• Kafka Integration
• Cloud Storage Integration
• Enterprise Data Pipelines

Module 18: Cloud Deployment

• Spark on Cloud
• Managed Spark Services
• Container Deployment
• Scaling Applications
• Deployment Best Practices

Module 19: Testing Spark Applications

• Unit Testing
• Integration Testing
• Data Validation
• Performance Testing
• Debugging Strategies

Module 20: Spark Application Development

• Project Structure
• Code Organization
• Development Best Practices
• Reusable Components
• Documentation

Module 21: Production Best Practices

• Deployment Strategies
• Monitoring
• Logging
• Performance Management
• Production Readiness

Module 22: Apache Spark Developer Project

• Distributed Data Processing
• Spark SQL Implementation
• Streaming Data Pipeline
• Performance Optimization
• Application Deployment