3 docs tagged with "Real-Time Data"

Learn all techniques for handling missing or null data in PySpark DataFrames including dropping nulls, filling values, conditional replacement, and computing statistics.

Imagine you’re a data scientist in a high-tech lab, not just a data engineer. Data isn’t sitting quietly in files—it’s streaming, growing, and changing constantly. You want to predict outcomes, classify users, or group behaviors, all at scale.

Learn the core components of PySpark—SparkSession, SparkContext, and configurations—and how they form the foundation of big data processing.