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Spark Interview Questions

Apache Spark is one of the most progressing technologies in the analytics domain at the present time. Owing to its prominence in handling the applications of Big Data many companies are relying on it for their data management applications.

If you are among those aspirants who are working towards clearing Spark interview then this blog post will definitely help you regarding the same.  In this blog post we have covered some of the most prominent & frequently asked interview questions on Spark domain.

Let’s have a look at the most frequently asked Apache Spark interview questions

  • Explain About Apache Spark

Apache Spark is an open-source cluster computing framework which is used for processing clusters of data in real-time. It supports an interface that lets users to program entire clusters with implicit data parallelism and fault-tolerance.

  • Explain The Key Features Of Apache Spark

The key features of Apache Spark technology are

  • Polyglot-

Supports high-level APIs in Java, Scala, Python and R. It supports its code to be written in any of these four languages.

  • Speed-

It can run several 100 times faster compared to Hadoop MapReduce

  • Multiple Format Support-

Supports multiple data sources like Parquet, JSON, Hive and Cassandra

  • Real Time Computation

Its in-memory computation is carried out in real-time & so it exhibits very less latency

  • Hadoop Integration

Spark is well compatible with Hadoop framework

  • Machine Learning

When it comes to handling Big Data processing then using Spark MLlib would prove to be ideal

  • What is a Sparse Vector?

A sparse vector is having two parallel arrays. The first array is for indices whereas the other array is for values. These vectors are used for storing non-zero entries to save space.

  • Which Languages Does Apache Spark Support For Developing Big Data Applications?

Apache Spark supports Scala, Java, Python, R and Clojure

  • Why Is BlinkDB Used?

BlinkDB is a query engine for executing interactive SQL queries on huge volumes of data and renders query results marked with meaningful error bars. It assists the users towards balancing ‘query accuracy’ with response time.

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