Apache Spark & Big Data Analytics
Learn Apache Spark & Big Data Analytics with practical skills in PySpark, distributed data processing, real-time streaming, machine learning, and Spark optimization.
Course Overview
Course Modules Module 1: Big Data Fundamentals Big Data 5 Vs Distributed Computing Hadoop Ecosystem HDFS Architecture MapReduce Paradigm Hive & HBase Module 2: Apache Spark Architecture Spark Components RDD vs DataFrame vs Dataset Spark Execution Model DAG & Lineage Memory Management Cluster Managers Module 3: PySpark for Data Processing SparkSession Setup DataFrame Operations SQL with Spark UDFs & Transformations Joins in Spark Reading/Writing Data Module 4: Spark Streaming Structured Streaming DStream API Kafka Integration Windowing Operations Stateful Processing Checkpointing Module 5: Spark ML & Optimization MLlib Algorithms Feature Engineering Pipeline Model Training at Scale Spark Tuning Partitioning Strategy Broadcast Variables Module 6: Capstone Project Dataset Ingestion Batch Processing Pipeline Streaming Component ML Model Training Performance Tuning Cluster Deployment Capstone Project Build a scalable big data pipeline using PySpark covering batch processing, streaming, and machine learning at scale.
Learning Outcomes
Process massive datasets efficiently using Apache Spark.
Implement real-time streaming with Spark Streaming and Kafka.
Apply MLlib for distributed machine learning.
Tune and optimize Spark jobs for performance.
Integrate Spark with cloud-based data platforms.
Prerequisites
Participants should have: Solid Python programming skills SQL query writing experience Understanding of distributed computing concepts
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