Tag: big_data_interview
Nuances of persist() and cache() in PySpark and learn when to use each .
Apache Spark, offers two methods for persisting RDDs (Resilient Distributed Datasets): persist() and cache(). Both are used to improve performance…
SparkContext vs. SparkSession: Understanding the Key Differences in Apache Spark
Apache Spark offers two fundamental entry points for interacting with the Spark engine: SparkContext and SparkSession. They serve different purposes…
Discover the significance of SparkSession in Apache Spark and how to create SparkSession
Apache Spark has become a cornerstone in the world of big data processing and analytics. To harness its power effectively,…
Converting RDDs to DataFrames in Apache Spark: A Step-by-Step Guide
Apache Spark is a powerful tool for big data processing, offering versatile data structures like Resilient Distributed Datasets (RDDs) and…
Understanding the differences between RDD and DataFrame in Apache Spark
Apache Spark has emerged as a powerful framework for big data processing, offering various data structures to manipulate and analyze…
DataFrames in PySpark: A Comprehensive Guide
Introduction to PySpark DataFrames PySpark, the Python API for Apache Spark, is renowned for its ability to handle big data…
Counting Null or None or Missing values with Precision in PySpark.
This article provides a comprehensive guide on how to accomplish this, a crucial step in data cleaning and preprocessing. Identifying…
How to derive the schema of a JSON string in PySpark
The schema_of_json function in PySpark is used to derive the schema of a JSON string. This schema can then be…
Reversing strings in PySpark
PySpark, the Python API for Apache Spark, is a powerful tool for large-scale data processing. In this guide, we explore…
Duplicating rows or values in a DataFrame
Data repetition in PySpark involves duplicating rows or values in a DataFrame to meet specific data analysis requirements. This process…
