Capgemini Hadoop Technical Interview Questions Answers

Capgemini Hadoop Technical Interview Questions and Answers

With the rise of Big Data technologies, Hadoop has become one of the most sought-after skills in the IT industry. Companies like Capgemini regularly hire Big Data Engineers, Hadoop Developers, and Data Analysts. Their interviews test not just theoretical knowledge but also the ability to solve real-time business problems using Hadoop’s ecosystem.

Below is a comprehensive set of Capgemini Hadoop interview questions and answers, ranging from basics to advanced.

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1. Basic Hadoop Interview Questions

Q1. What is Hadoop, and why is it used?
Hadoop is an open-source framework that allows storing and processing large datasets across clusters of computers. It is designed to handle massive data volumes, fault tolerance, and scalability.

Q2. What are the main components of Hadoop?

• HDFS (Hadoop Distributed File System) – Storage layer

• YARN (Yet Another Resource Negotiator) – Resource management and job scheduling

• MapReduce – Data processing framework

• Hadoop Common – Common utilities

Q3. What are the advantages of Hadoop over traditional RDBMS?

• Handles unstructured, semi-structured, and structured data

• Scales horizontally using commodity hardware

• Fault-tolerant (replication of data blocks)

• Supports batch and real-time processing

Q4. What is HDFS, and how does it work?
HDFS is Hadoop’s storage layer. Data is split into blocks (default 128MB/256MB) and distributed across multiple nodes. Each block is replicated (default replication factor = 3) to ensure fault tolerance.

Q5. Explain the difference between NameNode and DataNode.

• NameNode: Master node that stores metadata about files and directories.

• DataNode: Worker nodes that store the actual data blocks.

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2. Intermediate Hadoop Questions

Q6. What is YARN in Hadoop?
YARN (Yet Another Resource Negotiator) is the cluster resource management system in Hadoop. It manages resources and schedules tasks across nodes.

Q7. Explain the role of Secondary NameNode.
It is often misunderstood as a backup for NameNode, but it’s not. The Secondary NameNode periodically merges edit logs with the file system image to prevent the NameNode from having large edit logs.

Q8. What is MapReduce in Hadoop?
MapReduce is a programming model used for distributed data processing:

• Map phase: Processes input data into key-value pairs.

• Reduce phase: Aggregates the mapped results.

Q9. What are the different file formats in Hadoop?

• Text files (CSV, TSV)

• Sequence files

• Avro files

• Parquet (columnar storage, efficient for analytics)

• ORC (Optimized Row Columnar, common in Hive)

Q10. How does Hadoop ensure fault tolerance?

• Data replication across multiple nodes

• Heartbeat signals from DataNodes to NameNode

• Speculative execution of tasks to handle stragglers

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3. Advanced Hadoop Interview Questions

Q11. Explain the difference between Hive and Pig.

• Hive: SQL-like query language (HQL) used for data warehousing and structured queries.

• Pig: Scripting language (Pig Latin) used for data transformation and ETL processes.

Q12. What is the difference between HDFS and S3 (Amazon Simple Storage Service)?

• HDFS is designed for on-premise Hadoop clusters.

• Amazon S3 is cloud-based storage but can be integrated with Hadoop for scalability and cost-effectiveness.

Q13. What is the role of ZooKeeper in Hadoop?
ZooKeeper provides coordination services like maintaining configuration, naming, synchronization, and leader election for distributed systems such as HBase and Kafka.

Q14. How does Hadoop handle small files problem?
Since Hadoop is optimized for large files, too many small files can overload the NameNode. Solutions include:

• HAR (Hadoop Archive)

• SequenceFiles (to combine small files)

• Hive or HBase to store small data efficiently

Q15. Explain HBase and how it integrates with Hadoop.
HBase is a NoSQL database built on HDFS. It provides real-time read/write access to large datasets, unlike HDFS, which is optimized for batch processing.

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4. Real-Time Scenario Questions

Q16. Suppose a DataNode fails in Hadoop. What happens?

• NameNode detects failure via missing heartbeats.

• Data is still available from replicated copies on other DataNodes.

• Hadoop automatically replicates missing blocks to maintain fault tolerance.

Q17. How would you optimize a Hadoop job that is running very slowly?

• Increase the number of reducers.

• Use combiner functions to reduce shuffle data.

• Optimize data locality (process data where it resides).

• Use Parquet/ORC file formats for faster reads.

Q18. How do you implement data security in Hadoop?

• Authentication via Kerberos.

• File permissions in HDFS.

• Data encryption (at rest and in transit).

• Apache Ranger or Sentry for fine-grained access control.

Q19. How do you load data into HDFS?

• Using HDFS command line (hdfs dfs -put)

• Using Flume (for streaming data)

• Using Sqoop (for importing from RDBMS)

• Using Kafka (for real-time ingestion)

Q20. Can Hadoop handle real-time processing?
Hadoop’s MapReduce is batch-oriented, but real-time is possible using:

• Apache Spark

• Storm

• Kafka + HBase

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Final Thoughts

Capgemini Hadoop interviews are structured to test both fundamentals and practical expertise. You must understand the Hadoop ecosystem (HDFS, YARN, MapReduce, Hive, Pig, HBase, ZooKeeper, Spark) along with performance optimization and real-time use cases.

If you showcase hands-on experience with data ingestion, storage, and processing in Hadoop, along with problem-solving skills, you will stand out in the interview.