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Jan 29, 2024 · Apache Spark and Hadoop are both big data frameworks, but they differ significantly in their approach and capabilities. Let’s delve into a detailed comparison before presenting a comparison table for quick reference.
- How does Spark differ from Hadoop, and what advantages does it offer for big data processing? Spark differs from Hadoop primarily in its data processing approach and performance.
- Can you explain the architecture of Spark, highlighting the roles of key components such as the Driver Program, Cluster Manager, and the Executors? Apache Spark’s architecture follows a master/worker paradigm, with the Driver Program acting as the master and Executors as workers.
- What is the role of the DAG scheduler in Spark, and how does it contribute to optimizing query execution? The DAG scheduler in Spark plays a crucial role in optimizing query execution by transforming the logical execution plan into a physical one, consisting of stages and tasks.
- What are the key differences between RDD, DataFrame, and Dataset in Spark, and when would you choose to use each one? RDD (Resilient Distributed Dataset) is Spark’s low-level data structure, providing fault tolerance and parallel processing.
1. Apache Spark Interview Questions – Objective. Apache Spark is prevailing because of its capability to handle real-time streaming and processing big data faster than Hadoop MapReduce.
- What is the spark? Spark is a general-purpose in-memory compute engine. You can connect it with any storage system like a Local storage system, HDFS, Amazon S3, etc.
- What is RDD in Apache Spark? RDDs stand for Resilient Distributed Dataset. It is the most important building block of any spark application. It is immutable.
- What is the Difference between SparkContext Vs. SparkSession? In Spark 1.x version, we must create different contexts for each API. For example:- SparkContext.
- What is the broadcast variable? Broadcast variables in Spark are a mechanism for sharing the data across the executors to be read-only. Without broadcast variables, we have to ship the data to each executor whenever they perform any type of transformation and action, which can cause network overhead.
Jun 27, 2024 · Essential Spark interview questions with example answers for job-seekers, data professionals, and hiring managers. Jun 27, 2024. Apache Spark is a unified analytics engine for data engineering, data science, and machine learning at scale. It can be used with Python, SQL, R, Java, or Scala.
Sep 30, 2024 · Spark can run on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud, and can access data from multiple sources. And this article covers the most important Apache Spark Interview questions that you might face in a Spark interview.
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Apr 11, 2024 · Regarding the differences between these two systems: While Apache Hadoop permits you to join several computers together to analyze vast data sets faster, Apache Spark allows you to make speedy analytic queries within data sets ranging from large to small. Spark accomplishes this by utilizing in-memory caching along with advanced query performance.
