Feature Hadoop 1 Hadoop 2 Hadoop 3 NameNode Single point of failure Multiple NameNodes, active-standby Multiple NameNodes, active-active Secondary NameNode Not required Required Not required JobTracker Single point of failure… Read more »
The Role of YARN in Apache Spark: Resource Management and Architecture Explained YARN is a crucial part of the Apache Hadoop ecosystem that serves as a resource management framework for… Read more »
In Spark, there are two types of nodes: active nodes and passive nodes. Active nodes are nodes that process data and run tasks, while passive nodes are nodes that are… Read more »
Hadoop Distributed File System (HDFS) is a system that stores big data across many computers. The main part is the NameNode, which keeps track of files and controls who can… Read more »
Using the read.csv() method: This is the most common way to read a CSV file in PySpark. You can specify the file path, delimiter, header, and other options as parameters…. Read more »
When working with data in PySpark, you need to define the structure of the DataFrame. You can do this in two ways: by using StructType schema or by setting the… Read more »
Join optimization in PySpark is the process of improving the performance of join operations between two datasets. Join operations can be expensive, especially when dealing with large datasets, and optimizing… Read more »
Permissions: Ensure that you have the correct permissions to write to the location where you’re trying to store the data. You may need to check the access control settings in… Read more »
Healthcare: Big data is used to improve patient care, track and analyze medical data, and identify patterns and potential health risks. Finance: Big data is used to analyze financial data,… Read more »
Join optimization is a technique used in PySpark to improve the performance of join operations between two RDDs (Resilient Distributed Datasets). Join operations can be computationally expensive, especially when working… Read more »