Big Data

Big Data Interview Questions and Answers

Updated 6 Jul 2025

Asked in TCS

6d ago

Q. Practical implementation of Big Data examples

Ans.

Big Data is practically implemented in various industries like healthcare, finance, retail, and transportation.

  • In healthcare, Big Data is used for analyzing patient data to improve treatment outcomes and develop personalized medicine.

  • In finance, Big Data is used for fraud detection, risk analysis, and algorithmic trading.

  • In retail, Big Data is used for customer segmentation, demand forecasting, and inventory management.

  • In transportation, Big Data is used for optimizing routes...read more

Asked in InfoObjects

5d ago

Q. What is the difference between repartition and coalesce?

Ans.

Repartition increases or decreases the number of partitions in a DataFrame, while coalesce only decreases the number of partitions.

  • Repartition involves shuffling data across the network, while coalesce tries to minimize shuffling by only creating new partitions if necessary.

  • Repartition is typically used when increasing the number of partitions for parallelism, while coalesce is used for decreasing partitions to optimize performance.

  • Example: df.repartition(10) vs df.coalesce(5...read more

Big Data Interview Questions and Answers for Freshers

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4d ago

Q. What is the architecture of Spark?

Ans.

Spark architecture is a distributed computing framework that consists of a cluster manager and worker nodes.

  • Consists of a cluster manager (e.g. Spark Standalone, YARN, Mesos) for resource management

  • Worker nodes execute tasks and store data in memory or disk

  • Supports various programming languages like Scala, Java, Python, and SQL

  • Uses Resilient Distributed Datasets (RDDs) for fault-tolerant distributed data processing

Asked in ShikshaLokam

2d ago

Q. What is a Broadcast Join?

Ans.

Broadcast join is a type of join operation in big data processing where one of the datasets is small enough to be broadcasted to all nodes in the cluster.

  • Used when one dataset is small enough to fit in memory of all nodes

  • Reduces shuffling of data across the network

  • Improves performance by avoiding data transfer over the network

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