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Internet Of Things
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Big Data & Analytics
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Interview Questions on Data Science
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Interview Questions on Data Engineering
- Top 50+ ETL Interview Questions For Data Engineering
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Apache Spark Interview questions and Answers
Meet the Author : Mr. Bharani Kumar
Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 18+ years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.
How is Apache Spark different from MapReduce?
- a) Spark is open-source where MapReduce is commercialized
- b) MapReduce is fault-tolerant and Spark isnt
- c) "Both of the platforms support real-time processing"
- d) "Spark is In-memory computation whereas MapReduce is Disk-based computation "
Answer - d) "Spark is In-memory computation whereas MapReduce is Disk-based computation "
Apache Spark and MapReduce are very different in several features like Data Processing - Spark handles the processing in-memory whereas MapReduce is disk-based. Speed - MapReduce is very slow. Spark is considered to be 100x faster than MapReduce computation. MapReduce supports only low-level programming whereas Spark has multiple language support (Scala, Java, Python, SQL, and R). These two platforms are also different in the capabilities of Real-time and Batch mode operations support. Spark supports both the modes whereas MapReduce has only Batch-mode operations capability.
Which of these are not Apache Spark Features?
- a) Lazy Evaluation
- b) Real-time Processing
- c) Batch-mode Processing only
- d) In-Memory Computation
Answer - c) Batch-mode Processing only
Apache Spark is known as a super-fast in-memory cluster computing framework. It has many features which make it the first choice for Data Analysts, Data Engineers, and Data Scientists. Low Latency: Apache Spark helps in the achievement of a very high processing speed of data by reducing read-write operations to disk. The speed is almost 100x faster while performing in-memory computation and 10x faster while performing disk computation. In-Memory Computation: The in-memory computation feature of Spark increases the speed of data processing. It uses Data flow lineage graphs called DAG to speed-up data processing. Batch-mode and Real-time: Spark codes can be reused for batch-processing, data streaming, running ad-hoc queries, etc. Fault Tolerance: Spark supports fault tolerance. It uses special data abstractions called RDDs which are memory abstractions of the data, t