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- Spark has better processing speed, machine learning and the ability for performing iterative jobs. It also runs independently of Hadoop, which MapReduce does not. MapReduce relies on hard disk storage while Spark relies on in-memory storage, which is a generally more dependable way to store data."
www.indeed.com/career-advice/interviewing/spark-interview-questionsTop 12 Apache Spark Interview Questions (With Example Answers)
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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.
Speed – For large-scale data processing, Spark can be up to 100 times faster than Hadoop MapReduce. Apache Spark is able to achieve this tremendous speed via controlled portioning.
- Spark Core acts as the base engine for large-scale parallel and distributed data processing. It is the distributed execution engine used in conjunc...
- GraphX – Implements graphs and graph-parallel computationMLib – Used for machine learningSpark Core – Base engine used for large-scale parallel and...
- GraphX is the API used for implementing graphs and graph-parallel computing in Apache Spark. It extends the Spark RDD with a Resilient Distributed...
- Spark SQL modules help in integrating relational processing with Spark’s functional programming API. It supports querying data via SQL or HiveQL (H...
- Parquet is a columnar format that is supported by several data processing systems. With it, Spark SQL performs both read as well as write operation...
- Apache Mesos – Commonly used cluster managerStandalone – A basic cluster manager for setting up a clusterYARN – Used for resource management
- Yes, it is possible to use Apache Spark for accessing as well as analyzing data stored in Cassandra databases using the Spark Cassandra Connector....
- Enhanced Speed – MapReduce makes use of persistent storage for carrying out any of the data processing tasks. On the contrary, Spark uses in-memory...
- Having compatibility with Hadoop is one of the leading advantages of Apache Spark. The duo makes up for a powerful tech pair. Using Apache Spark an...
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.
- 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.
- 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.
Sep 16, 2024 · 16 Top Apache Spark Interview Questions with Answers. 16.1 About Editorial Team. 16.2 You Might Also Like: What is Apache Spark, and how does it differ from Hadoop MapReduce?
Jul 28, 2023 · Apache Spark is designed as an interface for large-scale processing, while Apache Hadoop provides a broader software framework for the distributed storage and processing of big data.
