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- 1. PySpark Architecture & Components Understanding the Engine Behind Big Data Processing
- 2. Agenda Introduction to PySpark Apache Spark Architecture Overview PySpark Architecture Key Components of PySpark Execution Flow Use Cases Summary & Q&A
- 3. What is PySpark? PySpark is the Python API for Apache Spark Enables writing Spark applications using Python Ideal for: Data engineering Machine learning at scale Big data analytics Supports distributed processing of large datasets
- 4. Apache Spark Architecture Overview Cluster-based computing system Core Components: Driver Program Cluster Manager (e.g., YARN, Mesos, Kubernetes) Executors Tasks Built on RDD (Resilient Distributed Datasets) & DAG Scheduler
- 6. Key Components of PySpark Component Description SparkContext Entry point to Spark functionality RDD Low-level distributed collection of objects DataFrame Distributed table with named columns (like Pandas) SparkSession Unified entry point for Spark 2.x+ Transformations Lazy operations (e.g., map, filter) Actions Triggers execution (e.g., collect, count)
- 7. Execution Flow in PySpark SparkSession Initiated 1. Driver Program defines RDDs/DataFrames 2. Transformations applied (Lazy) 3. Action triggers DAG creation 4. Tasks sent to Cluster Manager 5. Executors run tasks on workers 6. Results returned to driver 7.
- 8. PySpark Ecosystem Extensions MLlib: Machine learning at scale GraphX (via Scala/Java): Graph processing Spark SQL: SQL engine for querying structured data Spark Streaming: Real-time data processing Delta Lake (Databricks): ACID transactions on big data
- 9. Common Use Cases ETL Pipelines Data Warehousing Machine Learning Model Training Log Processing & Real-time Analytics Large-scale Data Exploration
- 10. Summary PySpark bridges Python with the power of distributed Spark Key concepts: Driver, Executors, RDDs, DataFrames, DAG Best for scalable data engineering & ML Py4J enables Python-JVM interaction behind the scenes Questions & Discussion Let’s dive deeper into anything you're curious about!