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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.
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Oct 25, 2024 Last updated |
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