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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.
Oct 26, 2024 · 26 Oct, 2024 - By Hoang Duyen. To help you thoroughly prepare for your next AWS interview, we've compiled a comprehensive guide with the Top 51 Must-Ask AWS Interview Questions and Detailed Answers. These questions range from basic to advanced, suitable for candidates with varying levels of experience.
23 Apache Spark Interview Questions (ANSWERED) To Learn Before ML & Big Data Interview | MLStack.Cafe. 1928 Curated Machine Learning, Data Science, Python & LLMs Interview Questions. Answered To Get Your Next Six-Figure Job Offer. See All ML Questions. AWS Machine Learning 30. Anomaly Detection 47. Apache Spark 30. Autoencoders 13. Azure ML 30.
Feb 6, 2023 · Hadoop reads and writes files to HDFS, Spark processes data in RAM using a concept known as an RDD, Resilient Distributed Dataset. Spark can run either in stand-alone mode, with a Hadoop cluster serving as the data source, or in conjunction with Mesos.
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Nov 6, 2023 · 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.
