Search results
Feb 6, 2023 · Apache Spark is a lightning-fast unified analytics engine used for cluster computing for large data sets like BigData and Hadoop with the aim to run programs parallel across multiple nodes. It is a combination of multiple stack libraries such as SQL and Dataframes, GraphX, MLlib, and Spark Streaming.
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.
Jul 28, 2023 · For most implementations, Apache Spark will be significantly faster than Apache Hadoop. Built for speed, Apache Spark may outcompete Apache Hadoop by nearly 100 times the speed.
- How is Apache Spark different from MapReduce? Apache Spark. MapReduce. Spark processes data in batches as well as in real-time. MapReduce processes data in batches only.
- What are the important components of the Spark ecosystem? Apache Spark has 3 main categories that comprise its ecosystem. Those are: Language support: Spark can integrate with different languages to applications and perform analytics.
- Explain how Spark runs applications with the help of its architecture. This is one of the most frequently asked spark interview questions, and the interviewer will expect you to give a thorough answer to it.
- What are the different cluster managers available in Apache Spark? Standalone Mode: By default, applications submitted to the standalone mode cluster will run in FIFO order, and each application will try to use all available nodes.
Jun 8, 2023 · Beginner. Most Asked Interview Questions on Apache Spark. Kishan Yadav Last Updated : 08 Jun, 2023. 7 min read. Introduction. Apache S park is an open-source unified analytics engine for large-scale data processing. Spark’s in-memory data processing capabilities make it 100 times faster than Hadoop.
People also ask
Is Apache Spark faster than Hadoop?
Does spark work with Hadoop?
Why do companies use Apache Spark?
What is Apache Spark best suited for?
What are the two major big data players – Apache Spark & Hadoop?
How secure is Hadoop vs spark?
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.
