Overview of Apache Spark and PySpark.pptx

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1. Overview Apache PySpark

2. What is Apache Spark? Apache Spark is an open-source, distributed computing system designed for big data processing. Developed by UC Berkeley in 2009, later became an Apache project. Supports batch and real-time data processing. Key Features: In-memory computation. Fault tolerance. Scalability across clusters. Multiple language support (Python, Scala, Java, R).

3. What is PySpark ? PySpark is the Python API for Apache Spark. Enables Python developers to leverage Spark’s power without needing Scala or Java. Integrates well with Python libraries like Pandas, NumPy, and ML frameworks. Supports: Spark Core (RDDs, DataFrames , Datasets). Spark SQL (Structured Query Language for big data). Spark MLlib (Machine Learning library). Spark Streaming (Real-time data processing).

4. Why Use PySpark ? Speed: Faster than traditional Hadoop MapReduce due to in-memory computing. Ease of Use: Python-friendly API with SQL-like queries. Scalability: Handles large datasets across distributed clusters. Flexibility: Works with various data sources (HDFS, S3, databases, etc.). Machine Learning: Built-in MLlib for AI/ML applications. Uses of PySpark

5. Apache Spark vs. PySpark Feature Apache Spark (Core) PySpark Language Scala, Java Python Ease of Use Complex Easier Performance High Slightly Lower (due to Python overhead) API Support Full Spark API Python API (some limitations)

6. Conclusion Apache Spark is a powerful big data framework for distributed processing. PySpark makes Spark accessible to Python developers. Ideal for large-scale data analytics, machine learning, and real-time processing.