Broadcast Variable
Darshini
A broadcast variable in PySpark is a mechanism for efficiently sharing read-only data across all nodes in a cluster. It is especially useful when you have data that needs to…
Pyspark
Aggregations
Aggregations in PySpark involve performing summary computations on data, such as calculating sums, averages, counts, or other statistical measures. These operations are often used to gain insights from datasets, such…
Spark UI
Setting up the Spark History Server on an Amazon EC2 instance involves configuring the Spark History Server to track and display Spark application logs. Here’s a step-by-step guide: Prerequisites Spark…
withColumn
In PySpark, the withColumn method is used to add a new column, modify an existing column, or replace an existing column in a DataFrame. It is one of the primary…
when
In PySpark, the when function is a crucial part of the pyspark.sql.functions module that helps implement conditional logic in a DataFrame transformation pipeline. Here’s an in-depth explanation of its usage,…
Pyspark Joins
PySpark, the Python API for Apache Spark, provides multiple join types to combine DataFrames based on specific conditions. These join types are crucial for merging datasets, performing lookups, or combining…
SparkContext and SparkSession in Apache Spark
Both SparkContext and SparkSession are foundational elements in Spark, but they serve different purposes and evolved in different versions of Spark. Let’s break down each independently before discussing their differences.…
PySpark Transformations: RDD and DataFrame Comparison Table
Explore a detailed comparison of PySpark transformations with a comprehensive table highlighting key points for RDD and DataFrame operations. Learn the differences, use cases, and examples for efficient big data…
Transformations
PySpark is a robust framework for big data processing, offering two main abstractions: RDD (Resilient Distributed Dataset) and DataFrame. Transformations in PySpark are operations applied to these datasets to produce…
Partitioning and Bucketing
Partitioning and bucketing are essential techniques in big data systems like Hive, Spark, and Hadoop, used to optimize query performance and data management. Both approaches enhance performance, but they serve…