AI & ML Exam > AI & ML Videos > Apache Spark: Master Machine Learning > Spark Interview Questions and Answers | Apache Spark Interview Questions | Spark Tutorial | Edureka
Spark Interview Questions and Answers | Apache Spark Interview Questions | Spark Tutorial | Edureka Video Lecture | Apache Spark: Master Machine Learning - AI & ML
FAQs on Spark Interview Questions and Answers - Apache Spark Interview Questions - Spark Tutorial - Edureka Video Lecture - Apache Spark: Master Machine Learning - AI & ML
| 1. What is Apache Spark and why is it used? |  |
Ans. Apache Spark is an open-source distributed computing system used for big data processing and analytics. It provides a unified analytics engine that allows users to process large-scale data sets with speed and efficiency. Spark is used for real-time stream processing, machine learning, graph processing, and interactive queries, making it a popular choice for data-intensive applications.
| 2. What are the key features of Apache Spark? | |
Ans. Apache Spark offers several key features, including:
- In-memory processing: Spark stores intermediate data in memory, reducing disk I/O and enabling faster data processing.
- Distributed computing: Spark allows users to distribute data and computation across a cluster of machines, enabling parallel processing and scalability.
- Fault tolerance: Spark automatically recovers from failures and continues processing, ensuring reliable and uninterrupted data processing.
- Spark SQL: Spark provides a SQL interface for querying structured data, making it easier for users familiar with SQL to work with Spark.
- Machine learning: Spark includes a scalable machine learning library (MLlib) for building and deploying machine learning models.
| 3. What is the difference between Apache Spark and Hadoop MapReduce? | |
Ans. While both Apache Spark and Hadoop MapReduce are used for big data processing, there are several key differences between the two:
- Processing speed: Spark performs in-memory processing, which makes it significantly faster than Hadoop MapReduce, which relies on disk-based processing.
- Ease of use: Spark provides a more user-friendly API and supports multiple programming languages, including Scala, Java, Python, and R. Hadoop MapReduce, on the other hand, primarily uses Java for programming.
- Real-time processing: Spark supports real-time stream processing, allowing users to process data as it arrives. Hadoop MapReduce is better suited for batch processing of large volumes of data.
- Data processing models: Spark provides higher-level APIs and libraries for various data processing tasks, such as SQL queries, machine learning, and graph processing. Hadoop MapReduce requires more manual coding for similar tasks.
| 4. How does Apache Spark handle fault tolerance? | |
Ans. Apache Spark handles fault tolerance through a mechanism called RDD (Resilient Distributed Datasets). RDDs are immutable distributed collections of objects that can be processed in parallel. Spark automatically tracks the lineage of RDDs, which allows it to recover lost data or compute lost RDD partitions in case of failures. RDDs are also designed to be fault-tolerant by nature, as they can be reconstructed from their lineage.
| 5. What are some common use cases of Apache Spark? | |
Ans. Apache Spark is used in various industries and domains for different purposes. Some common use cases of Spark include:
- Real-time stream processing: Spark can process and analyze streaming data in real-time, making it suitable for applications such as fraud detection, social media analysis, and IoT data processing.
- Machine learning: Spark's MLlib library allows users to build and deploy machine learning models at scale. This is useful for applications like recommendation systems, predictive analytics, and anomaly detection.
- ETL (Extract, Transform, Load): Spark can efficiently process and transform large volumes of data, making it ideal for ETL pipelines and data integration tasks.
- Interactive analytics: Spark's in-memory processing enables fast interactive queries on large datasets, making it suitable for ad hoc data analysis and exploratory data science tasks.
About this Video
4.84/5
Rating
Sep 28, 2025
Last updated
Related Exams
Video Description: Spark Interview Questions and Answers | Apache Spark Interview Questions | Spark Tutorial | Edureka for AI & ML 2025 is part of Apache Spark: Master Machine Learning preparation.
The notes and questions for Spark Interview Questions and Answers | Apache Spark Interview Questions | Spark Tutorial | Edureka have been prepared according to the AI & ML exam syllabus.
Information about Spark Interview Questions and Answers | Apache Spark Interview Questions | Spark Tutorial | Edureka covers all important topics for AI & ML 2025 Exam.
Find important definitions, questions, notes, meanings, examples, exercises and tests below for Spark Interview Questions and Answers | Apache Spark Interview Questions | Spark Tutorial | Edureka.
Introduction of Spark Interview Questions and Answers | Apache Spark Interview Questions | Spark Tutorial | Edureka in English is available as part of our Apache Spark: Master Machine Learning for AI & ML & Spark Interview Questions and Answers | Apache Spark Interview Questions | Spark Tutorial | Edureka in
Hindi for Apache Spark: Master Machine Learning course. Download more important topics related with notes, lectures and mock test series for
AI & ML Exam by signing up for free.
Description
Video Lecture & Questions for Spark Interview Questions and Answers | Apache Spark Interview Questions | Spark Tutorial | Edureka Video Lecture | Apache Spark: Master Machine Learning - AI & ML - AI & ML full syllabus preparation | Free video for AI & ML exam to prepare for Apache Spark: Master Machine Learning.
Information about Spark Interview Questions and Answers | Apache Spark Interview Questions | Spark Tutorial | Edureka
Here you can find the meaning of Spark Interview Questions and Answers | Apache Spark Interview Questions | Spark Tutorial | Edureka defined & explained in the simplest way possible.
Besides explaining types of Spark Interview Questions and Answers | Apache Spark Interview Questions | Spark Tutorial | Edureka theory, EduRev gives you an ample number of questions to practice Spark Interview Questions and Answers | Apache Spark Interview Questions | Spark Tutorial | Edureka tests,
examples and also practice AI & ML tests.
Related Searches
Previous Year Questions with Solutions
,past year papers
,MCQs
,Spark Interview Questions and Answers | Apache Spark Interview Questions | Spark Tutorial | Edureka Video Lecture | Apache Spark: Master Machine Learning - AI & ML
,Semester Notes
,Exam
,Extra Questions
,video lectures
,Important questions
,study material
,shortcuts and tricks
,Free
,practice quizzes
,Summary
,ppt
,Spark Interview Questions and Answers | Apache Spark Interview Questions | Spark Tutorial | Edureka Video Lecture | Apache Spark: Master Machine Learning - AI & ML
,mock tests for examination
,Objective type Questions
,Sample Paper
,Spark Interview Questions and Answers | Apache Spark Interview Questions | Spark Tutorial | Edureka Video Lecture | Apache Spark: Master Machine Learning - AI & ML
,Viva Questions
,
Study Spark Interview Questions and Answers | Apache Spark Interview Questions | Spark Tutorial | Edureka on the App
Students of AI & ML can study Spark Interview Questions and Answers | Apache Spark Interview Questions | Spark Tutorial | Edureka alongwith tests & analysis from the EduRev app,
which will help them while preparing for their exam. Apart from the Spark Interview Questions and Answers | Apache Spark Interview Questions | Spark Tutorial | Edureka,
students can also utilize the EduRev App for other study materials such as previous year question papers, syllabus, important questions, etc.
The EduRev App will make your learning easier as you can access it from anywhere you want.
The content of Spark Interview Questions and Answers | Apache Spark Interview Questions | Spark Tutorial | Edureka is prepared as per the latest AI & ML syllabus.
|
© EduRev
|
Education Revolution
|
|
Signup on EduRev and stay on top of your study goals
10M+ students crushing their study goals daily