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IBM says if any data can be characterized or classified by these 3 v’s (Volume, Variety and Velocity) then it is called as Big Data.
Volume : Scale of Data.
Variety : Different forms of data.
Velocity : Speed at which new data is generated.
IBM says if any data can be characterized or classified by these 3 v’s (Volume, Variety and Velocity) then it is called as Big Data.
Volume : Scale of Data.
Variety : Different forms of data.
Velocity : Speed at which new data is generated.
Renaming columns in a PySpark DataFrame is a common task when you’re cleaning, transforming, or organizing data. Whether you’re working with external datasets or need to make your DataFrame more readable, PySpark offers multiple ways to rename columns. In this article, we’ll cover three popular methods to rename columns in PySpark:
1) withColumnRenamed()
2) selectExpr()
3) select() with col()
In PySpark, adding a new column to a DataFrame is a common and essential operation, often used for transforming data, performing calculations, or enriching the dataset. PySpark offers 3 main methods for this: withColumn(),select() and selectExpr(). These methods allow you to create new columns, but they serve different purposes and are used in different contexts.
This article will guide you through adding new columns using both methods, explaining their use cases and providing examples.
In PySpark, a DataFrame is a distributed collection of data organized into named columns, similar to a table in a relational database or an Excel spreadsheet. DataFrames provide a powerful abstraction for working with structured data, offering ease of use, high-level transformations, and optimization features like catalyst and Tungsten. This article will cover how to […]
Resilient Distributed Datasets (RDDs) are the core abstraction in PySpark, offering fault-tolerant, distributed data structures that can be operated on in parallel. Although the DataFrame API is more popular due to its higher-level abstractions, RDDs are still fundamental for certain low-level operations and are the building blocks of PySpark.
In this article, you’ll learn how to create RDDs in PySpark, the different ways to create them, and when you should use RDDs over DataFrames.
In this article, we will see the list of questions asked in Accenture Company Interview for Azure Data Engineers.
Let’s see the Questions:
Creating a Spark session is the first step when working with PySpark, as it allows you to interact with Spark’s core functionality. This article will walk you through the process of creating a Spark session in PySpark.
In this post, we will extend that setup to include PySpark, allowing you to work with Spark using Python. Let’s dive into the steps to get PySpark running on your Windows machine!
Setting up Apache Spark on a Windows machine can be a straightforward process if you follow the right steps. This guide will walk you through installing Java, configuring environment variables, downloading and setting up Spark, and finally running Spark on your Windows system. Let’s get started!
In this article, we will see the list of questions asked in Wipro Company Interview for Data Engineers.
Let’s see the Questions:
1) Describe the concept of imputations (handling missing data) in Spark.
In this article, we’ll explore a list of AWS Glue interview questions commonly asked to candidates with 3+ years of experience. Let’s see the Questions.
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