Tag: Spark_Interview
Learn to use broadcast variables : Advanced Data Transformation in PySpark
PySpark script efficiently handles the transformation of country codes to their full names in a DataFrame. It begins by establishing…
Enhancing PySpark with Custom UDFRegistration
PySpark, the powerful Python API for Apache Spark, provides a feature known as UDFRegistration for defining custom User-Defined Functions (UDFs)….
Power of PySpark GroupedData for Advanced Data Analysis
GroupedData in PySpark is a powerful tool for data grouping and aggregation, enabling detailed and complex data analysis. Mastering this…
Efficient Data Cleaning with PySpark DataFrameNaFunctions
Leveraging PySpark for Data Integrity In the realm of big data, PySpark stands out as a powerful tool for processing…
PySpark DataFrameStatFunctions: Essential Tools for Data Analysis
PySpark, the Python API for Apache Spark, is a leading framework for big data processing. This article dives into one…
DataFrame operations to retrieve the first element in a group in PySpark
PySpark’s first function is a part of the pyspark.sql.functions module. It is used in DataFrame operations to retrieve the first…
PySpark’s Degrees Function : Convert values in radians to degrees
PySpark’s degrees function plays a vital role in data transformation, especially in converting radians to degrees. This article provides a…
PySpark’s DESC Function: DataFrame operations to sort data in descending order
PySpark, the Python API for Apache Spark, is widely used for its efficiency and ease of use. One of the…
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…
