Big Data Processing with Spark
Apache Spark has become the de facto standard for big data processing, offering speed, ease of use, and powerful analytics capabilities. It provides a unified engine for batch processing, streaming, machine learning, and graph processing.
Spark Architecture
Understand Spark's distributed computing model, including the driver program, cluster manager, and worker nodes. Learn how Spark achieves fault tolerance and parallel processing.
RDDs and DataFrames
Master Spark's core abstractions: Resilient Distributed Datasets (RDDs) for low-level control and DataFrames for high-level, SQL-like operations on structured data.
Batch Processing
Process large datasets efficiently using Spark's transformation and action operations. Learn optimization techniques like caching, partitioning, and broadcast joins.
Structured Streaming
Process real-time data streams with Spark Streaming and Structured Streaming. Handle late data, watermarking, and exactly-once processing semantics.
Spark SQL
Use SQL queries on DataFrames for familiar data manipulation. Learn to create temporary views, perform complex joins, and optimize query performance.
Machine Learning with MLlib
Build and deploy machine learning models at scale using Spark's MLlib. Cover classification, regression, clustering, and recommendation systems.
Performance Tuning
Optimize Spark applications through proper resource allocation, data serialization, and query optimization. Learn to identify and resolve common performance bottlenecks.
Production Deployment
Deploy Spark applications on various platforms including Hadoop YARN, Kubernetes, and cloud services. Learn monitoring, logging, and troubleshooting techniques.
