What is Apache Spark?
"Apache Spark is an open-source, distributed computing framework designed for big data processing. It was developed by UC Berkeley in 2009 and is now one of the most powerful tools for handling massive datasets."
🔥 Why is Spark So Popular?
- ✔️ 100x faster than Hadoop – Uses in-memory computing.
- ✔️ Supports multiple workloads – Batch, streaming, machine learning, and graph processing.
- ✔️ Scales easily – Runs on clusters with thousands of nodes.
What is PySpark?
"Now that we understand Apache Spark, let's talk about PySpark. PySpark is simply the Python API for Apache Spark, allowing us to use Spark with Python instead of Scala or Java."
💎 Why Use PySpark?
- ✔️ Python is easy to learn – Great for data engineers & scientists.
- ✔️ Leverages Spark’s speed – Handles big data in a scalable way.
- ✔️ Integrates with Pandas, NumPy, and Machine Learning libraries.
Apache Spark vs PySpark – Key Differences
| Feature | Apache Spark | PySpark |
|---|---|---|
| Language | Scala, Java | Python |
| Ease of Use | More complex | Easier for beginners |
| Performance | Faster (native) | Slightly slower (Python overhead) |
| Community Support | Strong (since 2009) | Growing rapidly |
| Best For | Large-scale data engineering | Python-based big data & ML |
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