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You can use Hadoop and Spark to benefit from the strengths of both frameworks. Hadoop provides secure and affordable distributed processing. If you run Spark on Hadoop, you can shift time-sensitive workloads, such as graph analytics tasks, to Spark’s in-memory data processors.
Jun 27, 2024 · Essential Spark interview questions with example answers for job-seekers, data professionals, and hiring managers.
- 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.
- 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.
Nov 6, 2023 · Vibhuthi Viswanathan. 11/06/2023. Hadoop vs. Spark: Choosing the Right Big Data Processing Framework. In the realm of big data, two titans stand tall: Hadoop and Apache Spark. These powerful frameworks have transformed the way organizations process and analyze vast datasets.
Follow along and learn the 23 most common and advanced Apache Spark interview questions and answers to prepare for your next big data and machine learning interview. Q1 : Briefly compare Apache Spark vs Apache Hadoop
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Jun 19, 2024 · Basic PySpark Interview Questions. Let's start by exploring some fundamental PySpark interview questions that assess your understanding of the core concepts and advantages of this powerful library. What are the main advantages of using PySpark over traditional Python for big data processing?
