What is PySpark?
Apache Spark is a unified analytics engine for large‑scale data processing. PySpark is the Python API for Spark, allowing you to write Spark applications using Python. Unlike Pandas, which runs on a single machine, PySpark distributes work across many machines (or cores) to handle terabytes of data.
PySpark = Python + Apache Spark. It combines Python’s simplicity with Spark’s distributed computing power.
Why PySpark?
- Process huge datasets (terabytes/petabytes) that don’t fit on a single machine.
- Faster than Pandas for large data because it runs in parallel.
- Unified API for batch processing, SQL, streaming, and machine learning.
- Works with Hadoop, cloud storage (S3, ADLS), and databases.
PySpark vs Pandas
- Pandas: single‑machine, in‑memory, great for up to ~10‑50 GB data.
- PySpark: distributed, scales to TB/PB, uses multiple cores/nodes.
- Pandas is easier for small data; PySpark is essential for big data.
Spark Architecture (High‑Level)
Spark applications have a driver (your PySpark script) and executors (worker processes). The driver splits work into tasks and sends them to executors, which process data in parallel.
Two Minute Drill
- PySpark is the Python API for Apache Spark.
- It handles distributed processing for big data.
- Faster and more scalable than Pandas for large datasets.
- Used for batch, SQL, streaming, and ML.
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