Top 10 Hadoop Interview Questions and Answers

Hadoop Architecture is a distributed computing framework that allows for the processing of large data sets.

• Hadoop consists of two main components: Hadoop Distributed File System (HDFS) and MapReduce.

• HDFS is responsible for storing data across multiple nodes in a cluster.

• MapReduce is responsible for processing the data stored in HDFS by dividing it into smaller chunks and processing them in parallel.

• Hadoop also includes other components such as YARN, which manages resources in...read more

Spark is faster than Hadoop MapReduce due to in-memory processing and supports multiple types of workloads.

• Spark performs in-memory processing, while Hadoop MapReduce writes to disk after each task.

• Spark supports multiple types of workloads like batch processing, interactive queries, streaming data, and machine learning, while Hadoop MapReduce is mainly for batch processing.

• Spark provides higher-level APIs in Java, Scala, Python, and R, making it easier to use than Hadoop Map...read more

NameNode is a component in Hadoop that manages the file system metadata and keeps track of the location of data blocks.

• NameNode is the master node in Hadoop's HDFS (Hadoop Distributed File System).

• It stores the metadata of all the files and directories in the HDFS.

• NameNode maintains the mapping of data blocks to DataNodes where the actual data is stored.

• It handles client requests for file operations like read, write, and delete.

• NameNode is a single point of failure in Hadoop,...read more

Hadoop is an open-source framework for distributed storage and processing of large data sets, while HDFS is the Hadoop Distributed File System used for storing data across multiple machines.

• Hadoop is designed to handle big data by distributing the data processing tasks across a cluster of computers.

• HDFS is the primary storage system used by Hadoop, which breaks down large files into smaller blocks and distributes them across multiple nodes in a cluster.

• HDFS provides high faul...read more

Hadoop is a distributed storage and processing system for big data, while Kafka is a distributed streaming platform.

• Hadoop is used for storing and processing large volumes of data across clusters of computers.

• Kafka is used for building real-time data pipelines and streaming applications.

• Hadoop uses HDFS (Hadoop Distributed File System) for storage, while Kafka uses topics to publish and subscribe to streams of data.

• Hadoop MapReduce is a processing framework within Hadoop, whi...read more

Hadoop is a distributed open-source framework for storing and processing large datasets, while GCP (Google Cloud Platform) is a cloud computing service that offers various data processing and storage solutions.

• Consider the size and complexity of your data: Hadoop is better suited for large-scale batch processing, while GCP offers more flexibility and scalability for various types of workloads.

• Evaluate your team's expertise: Hadoop requires specialized skills in managing and m...read more

Shuffle and merge are key processes in Hadoop for distributing data across nodes and combining results.

• Shuffle is the process of transferring data from mappers to reducers in Hadoop.

• Merge is the process of combining the output from multiple reducers into a single result.

• Shuffle and merge are essential for parallel processing and efficient data analysis in Hadoop.

• Example: In a word count job, shuffle will group words by key and send them to reducers, while merge will combine t...read more

Hadoop serialisation techniques are used to convert data into a format that can be stored and processed in Hadoop.

• Hadoop uses Writable interface for serialisation and deserialisation of data

• Avro, Thrift, and Protocol Buffers are popular serialisation frameworks used in Hadoop

• Serialisation can be customised using custom Writable classes or external libraries

• Serialisation plays a crucial role in Hadoop performance and efficiency

Hadoop is a distributed processing framework used for storing and processing large datasets across clusters of computers.

• Hadoop is designed to handle big data by distributing the workload across multiple machines.

• It consists of two main components: Hadoop Distributed File System (HDFS) and MapReduce.

• HDFS is a distributed file system that stores data across multiple nodes in a cluster.

• MapReduce is a programming model used for processing and analyzing the data stored in HDFS.

• Ha...read more

Spark is a fast and general-purpose cluster computing system, while Hadoop is a distributed processing framework.

• Spark is designed for in-memory processing, while Hadoop is disk-based.

• Spark provides real-time processing capabilities, while Hadoop is primarily used for batch processing.

• Spark has a more flexible and expressive programming model compared to Hadoop's MapReduce.

• Spark can be used with various data sources like HDFS, HBase, and more, while Hadoop is typically used w...read more

Hadoop architecture is a distributed computing framework for processing large data sets across clusters of computers.

• Hadoop consists of HDFS (Hadoop Distributed File System) for storage and MapReduce for processing.

• HDFS divides data into blocks and stores them across multiple nodes in a cluster.

• MapReduce is a programming model for processing large data sets in parallel across a distributed cluster.

• Hadoop also includes YARN (Yet Another Resource Negotiator) for resource manage...read more

Spark is a fast and distributed data processing engine that can perform in-memory processing.

• Spark is faster than Hadoop because it can perform in-memory processing, reducing the need to write intermediate results to disk.

• Spark uses DAG (Directed Acyclic Graph) for processing tasks, which optimizes the workflow and minimizes data shuffling.

• Spark allows for iterative computations, making it suitable for machine learning algorithms that require multiple passes over the data.

• Spa...read more

Spark is faster for real-time processing, while Hadoop is better for batch processing and large-scale data storage.

• Spark is faster than Hadoop due to in-memory processing.

• Hadoop is better for batch processing and large-scale data storage.

• Spark is more suitable for real-time processing and iterative algorithms.

• Hadoop is more suitable for processing large volumes of data in a distributed manner.

• Spark is commonly used for machine learning and streaming data processing.

• Hadoop is ...read more

Hadoop is a distributed storage system while Spark is a distributed processing engine.

• Hadoop is primarily used for storing and processing large volumes of data in a distributed environment.

• Spark is designed for fast data processing and can perform in-memory computations, making it faster than Hadoop for certain tasks.

• Hadoop uses MapReduce for processing data, while Spark uses Resilient Distributed Datasets (RDDs) for faster processing.

• Spark is more suitable for real-time proc...read more

Spark is a fast and general-purpose cluster computing system.

• Spark is designed for speed and ease of use in data processing.

• It can run programs up to 100x faster than Hadoop MapReduce.

• Spark provides high-level APIs in Java, Scala, Python, and R.

• It supports various workloads such as batch processing, interactive queries, streaming analytics, and machine learning.

• Spark can be used standalone, on Mesos, or on Hadoop YARN cluster manager.