Hadoop data analyst technical interview questions

Hadoop data analyst technical interview questions
  1. What is the significance of Hadoop's 'Writable' interface?

    • Answer: 'Writable' is a Hadoop-specific data serialization interface, used to serialize and deserialize data in a network-efficient format for MapReduce jobs.
  2. Can you explain the difference between Hadoop 2 and Hadoop 3?

    • Answer: Hadoop 3 introduces significant improvements like better storage efficiency, enhanced scalability, and support for more advanced processing paradigms compared to Hadoop 2.
  3. What are the benefits of using Apache Hive over an RDBMS?

    • Answer: Apache Hive provides scalability and support for large datasets that are typical in big data environments, which traditional RDBMS cannot efficiently handle.
  4. How do you secure a Hadoop cluster?

    • Answer: Securing a Hadoop cluster involves implementing Kerberos authentication, configuring firewalls, using HDFS encryption, and applying proper access control and auditing.
  5. What is the purpose of the Secondary NameNode in Hadoop?

    • Answer: The Secondary NameNode in Hadoop works alongside the NameNode to periodically merge changes with the main filesystem directory and helps in preventing data loss.
  6. Explain the concept of a "Reducer" in Hadoop's MapReduce.

    • Answer: The Reducer in MapReduce aggregates and processes the data emitted by Mappers, generating the final output of the MapReduce job.
  7. What is Apache Pig Latin?

    • Answer: Pig Latin is the scripting language used by Apache Pig to simplify the complexities of writing MapReduce programs.
  8. How does Hadoop handle node failures in a cluster?

    • Answer: Hadoop handles node failures by reassigning the tasks of the failed node to other nodes in the cluster and using the data replicas stored across different nodes.
  9. What is a Balancer in Hadoop?

    • Answer: A Balancer in Hadoop is a daemon that distributes data evenly across the Hadoop cluster to ensure that storage is utilized efficiently.
  10. Explain the role of the ResourceManager in YARN.

    • Answer: The ResourceManager in YARN is responsible for managing the cluster's resources and scheduling user applications.
  11. How does Apache Flume aid in Hadoop integration?

    • Answer: Apache Flume helps in efficiently collecting, aggregating, and moving large amounts of log data from different sources to HDFS.
  12. What is Hadoop's DistCp?

    • Answer: DistCp (Distributed Copy) is a tool used for large inter/intra-cluster data transfers in Hadoop.
  13. Can you explain the concept of InputSplit in Hadoop?

    • Answer: InputSplit defines the slice of data to be processed by a single Mapper in a MapReduce job, improving parallel processing.
  14. What are some common performance bottlenecks in Hadoop?

    • Answer: Common performance bottlenecks include inadequate memory resources, inefficient data formats, and unoptimized MapReduce algorithms.
  15. How is data staged in Hadoop's MapReduce process?

    • Answer: Data in MapReduce is staged in three steps: the map stage (for processing), the shuffle stage (for sorting and transferring data), and the reduce stage (for final processing).
  16. What is Sqoop in Hadoop?

    • Answer: Sqoop is a tool designed to transfer data between Hadoop and relational databases.
  17. Describe the 'Small Files Problem' in Hadoop.

    • Answer: The 'Small Files Problem' refers to the inefficiency caused in HDFS when dealing with a large number of small files, as each file is stored as a separate block, consuming excessive metadata storage.
  18. What is a NameNode Federation in Hadoop?

    • Answer: NameNode Federation is a Hadoop feature that allows multiple NameNodes to operate in the cluster, improving scalability and cluster management.
  19. How can you optimize a MapReduce job?

    • Answer: Optimizing a MapReduce job involves tuning the number of mappers and reducers, selecting the right data formats, and efficiently writing MapReduce algorithms.
  20. What is a Snapshot in Hadoop?

    • Answer: Snapshots in Hadoop are read-only point-in-time copies of the file system, used for data backup and recovery.