1. What is Hadoop?

Hadoop is an open-source framework for distributed storage and processing of large data sets using a cluster of commodity hardware.

2. What are the core components of Hadoop?

The core components of Hadoop are Hadoop Distributed File System (HDFS) for storage and MapReduce for processing.

3. What is Hadoop MapReduce?

Hadoop MapReduce is a programming model and processing engine for distributed data processing in Hadoop clusters. It consists of a Map phase and a Reduce phase.

4. What is Hadoop Distributed File System (HDFS)?

HDFS is the primary storage system used by Hadoop. It is designed to store large amounts of data across multiple nodes in a distributed manner.

5. How does Hadoop handle fault tolerance?

Hadoop achieves fault tolerance through data replication. It stores multiple copies of each data block across different nodes in the cluster.

6. What is the role of the NameNode in HDFS?

The NameNode in HDFS is responsible for managing metadata and namespace operations. It keeps track of the file system tree and the metadata for all files and directories.

7. What is the purpose of the DataNode in HDFS?

DataNodes in HDFS are responsible for storing and managing the actual data blocks. They receive instructions from the NameNode and serve read and write requests.

8. How does Hadoop perform data processing in parallel?

Hadoop performs parallel data processing by dividing large datasets into smaller chunks and processing them independently across multiple nodes in the cluster.

9. What are the limitations of Hadoop MapReduce?

Hadoop MapReduce has limitations in handling iterative algorithms, real-time processing, and complex workflows compared to more modern data processing frameworks.

10. What is the Hadoop ecosystem?

The Hadoop ecosystem is a collection of open-source tools and frameworks that complement Hadoop, providing additional functionalities such as data ingestion, processing, and analysis.

11. What is Apache Hive?

Apache Hive is a data warehouse infrastructure built on top of Hadoop. It provides a query language called HiveQL for querying and managing large datasets.

12. What is Apache Pig?

Apache Pig is a high-level platform and scripting language built on top of Hadoop. It simplifies the process of writing MapReduce programs using a scripting language called Pig Latin.

13. What is Apache HBase?

Apache HBase is a NoSQL, distributed database that provides real-time read and write access to large datasets. It is designed to store and manage massive amounts of sparse data.

14. What is Apache Spark?

Apache Spark is a fast and general-purpose cluster computing system that provides in-memory data processing capabilities. It can be used with Hadoop or as a standalone framework.

15. How does Apache Spark differ from Hadoop MapReduce?

Apache Spark performs in-memory processing, which makes it faster than Hadoop MapReduce. It also supports iterative algorithms and interactive data analysis.

16. What is Apache ZooKeeper used for in Hadoop?

Apache ZooKeeper is a distributed coordination service used in Hadoop clusters to manage and synchronize tasks, configurations, and distributed applications.

17. What is Apache Kafka?

Apache Kafka is a distributed streaming platform that can be integrated with Hadoop. It is used for building real-time data pipelines and streaming applications.

18. What is Apache Sqoop?

Apache Sqoop is a tool for efficiently transferring data between Hadoop and relational databases. It facilitates importing and exporting data to and from Hadoop.

19. How is security managed in Hadoop?

Hadoop provides security features such as authentication, authorization, and encryption. Kerberos is commonly used for authentication, and Access Control Lists (ACLs) manage authorization.

20. What is the purpose of the ResourceManager in Hadoop YARN?

The ResourceManager in Hadoop YARN (Yet Another Resource Negotiator) is responsible for managing and allocating resources in a Hadoop cluster.

21. What is the NodeManager in Hadoop YARN?

The NodeManager in Hadoop YARN is responsible for managing resources and containers on individual nodes in a Hadoop cluster.

22. What is Apache Mahout?

Apache Mahout is a machine learning library built on top of Hadoop. It provides scalable implementations of various machine learning algorithms.

23. What is the significance of the Secondary NameNode in HDFS?

The Secondary NameNode in HDFS is responsible for creating periodic checkpoints of the file system metadata. It does not act as a standby or backup for the primary NameNode.

24. What is Apache Oozie?

Apache Oozie is a workflow scheduler for managing Hadoop jobs. It allows users to define and execute workflows that consist of multiple Hadoop jobs.

25. What is Hadoop Streaming?

Hadoop Streaming is a utility that allows developers to use non-Java programming languages (e.g., Python, Ruby) to write MapReduce jobs for Hadoop.

26. What is Hadoop Rack Awareness?

Rack Awareness in Hadoop is a feature that ensures data replication across different racks in a data center to enhance fault tolerance and reduce data transfer latency.

27. How does Hadoop handle data locality?

Hadoop optimizes data locality by scheduling tasks on nodes where the data resides. This reduces the need for data transfer over the network.

28. What is the purpose of the Hadoop Fair Scheduler?

The Hadoop Fair Scheduler is a scheduler for Hadoop YARN that provides fair sharing of resources among multiple users and applications.

29. What is speculative execution in Hadoop?

Speculative execution in Hadoop is a feature where a task that is progressing slower than others is re-executed on another node to complete the job faster.

30. What is the purpose of the Hadoop Archive (HAR) file format?

The Hadoop Archive (HAR) file format is used to archive and compress large numbers of small files in HDFS, improving storage efficiency.

31. What is the Hadoop Capacity Scheduler?

The Hadoop Capacity Scheduler is a scheduler for Hadoop YARN that allows multiple organizations or users to share a Hadoop cluster based on allocated capacities.

32. How does Hadoop handle data skewness?

Data skewness in Hadoop, where some keys have significantly more data than others, can be addressed by using techniques like data partitioning, combiners, and custom partitioners.

33. What is the purpose of the Hadoop MapReduce Shuffle phase?

The Shuffle phase in Hadoop MapReduce is responsible for redistributing and consolidating the output of the Map tasks before it is sent to the Reduce tasks.

34. How does Hadoop ensure data integrity in HDFS?

Hadoop ensures data integrity in HDFS through data checksums. Each data block is associated with a checksum, and the integrity is verified during reads.

35. What is the Hadoop Trash feature?

The Hadoop Trash feature allows users to recover deleted files by moving them to a trash directory. Files are permanently deleted after a specified period.

36. What is the significance of the Hadoop SequenceFile format?

The Hadoop SequenceFile format is a binary file format optimized for storing large amounts of key-value pairs. It is often used as an intermediate format in MapReduce jobs.

37. How does Hadoop handle node failures in a cluster?

Hadoop handles node failures by redistributing tasks to healthy nodes and ensuring that data replication maintains fault tolerance.

38. What is the purpose of the Hadoop Chukwa project?

The Hadoop Chukwa project is a data collection and monitoring system that collects log data and metrics from Hadoop clusters.

39. What is the Hadoop Delegation Token?

The Hadoop Delegation Token is a security token used for secure communication between various components in a Hadoop cluster.

40. How can Hadoop be integrated with relational databases?

Hadoop can be integrated with relational databases using tools like Apache Sqoop for importing and exporting data between Hadoop and databases.

41. What is the Hadoop JobTracker?

The Hadoop JobTracker was the central daemon in Hadoop MapReduce v1 responsible for coordinating the execution of MapReduce jobs. It has been replaced by the ResourceManager in Hadoop YARN.

42. How can Hadoop be used for log processing and analysis?

Hadoop can be used for log processing and analysis by ingesting log files into HDFS and running MapReduce or Apache Spark jobs to extract insights from the log data.

43. What is Hadoop federation?

Hadoop federation is a feature that allows multiple independent Hadoop clusters to share a common namespace and storage space while maintaining isolation.

44. What is the purpose of Hadoop benchmarks?

Hadoop benchmarks are used to measure the performance of Hadoop clusters. Common benchmarks include TeraSort and TestDFSIO.

45. What is the Hadoop YARN ResourceManager High Availability (RM HA) feature?

The Hadoop YARN ResourceManager High Availability feature ensures the availability of the ResourceManager by providing multiple ResourceManager nodes in an active-standby configuration.

46. How does Hadoop handle job scheduling and prioritization?

Hadoop job scheduling and prioritization can be managed using features like fair schedulers, capacity schedulers, and job priorities.

47. What is Hadoop federation?

Hadoop federation is a feature that allows multiple independent Hadoop clusters to share a common namespace and storage space while maintaining isolation.

48. How does Hadoop handle data compression?

Hadoop handles data compression using codecs such as Gzip, Snappy, or LZO. Data compression helps reduce storage requirements and speeds up data transfer.

49. What is the purpose of the Hadoop MapReduce Combiner?

The Hadoop MapReduce Combiner is a mini-reducer that performs a local aggregation of output records from the Map tasks before they are sent to the Reduce tasks, reducing network traffic.

50. How does Hadoop handle data serialization and deserialization?

Hadoop uses serialization frameworks like Apache Avro or Apache Parquet to serialize data efficiently before storing it and deserialize it during processing. This helps in reducing storage and network overhead.