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🔍 *What is RDD in Apache Spark?*
RDD stands for *Resilient Distributed Dataset*. It is the fundamental data structure in Apache Spark, enabling fault-tolerant and parallel processing of large datasets across multiple nodes.

💡 *Syntax of RDD in PySpark*
sc.parallelize([1, 2, 3, 4, 5])
OR
sc.textFile("path_to_file")

📌 *Example*
data = sc.parallelize([10, 20, 30])
filtered_data = data.filter(lambda x: x != 15)
print(filtered_data.collect())

✅ *Tips*
Use RDDs when you need fine-grained control over your data transformations.
RDDs are immutable and *lazy evaluated*.
Prefer DataFrames for optimized performance, but fall back to RDDs for custom operations.

📘 *Official Documentation*
Apache Spark RDD Docs: https://spark.apache.org/docs/latest/rdd-p...

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