Crack Big Data Interview 2025: 50+ Real Q&A, Job Winning Tips & GuideCrack Big Data Interview 2025: 50+ Real Q&A, Job Winning Tips & GuideBIG DATA Interview Questions and Answers (2025) | JaganInfo

Big Data refers to extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

Volume (size of data), Velocity (speed of data generation), Variety (different data types), Veracity (data quality), and Value (usefulness of data).

Social media, sensors, IoT devices, transactional data, logs, multimedia files, and web data.

Hadoop is an open-source framework used for distributed storage (HDFS) and processing (MapReduce) of large data sets across clusters of computers.

Hadoop Distributed File System is a scalable and fault-tolerant file system designed to run on commodity hardware and store Big Data.

MapReduce is a programming model and processing technique for distributed computing, consisting of a Map step (filter and sort) and a Reduce step (summary and aggregation).

Apache Spark is an open-source distributed computing system that provides fast in-memory data processing for Big Data analytics.

HDFS, NoSQL databases (Cassandra, HBase), object storage systems (S3), and distributed file systems.

Data Lakes store vast amounts of raw data in its native format until it is needed for analysis.

Batch processing handles large volumes of data collected over time; real-time processing deals with data streams continuously and immediately.

Hadoop architecture consists of HDFS for storage and YARN for resource management, with MapReduce as processing framework.

YARN (Yet Another Resource Negotiator) is the cluster resource management layer in Hadoop that schedules jobs and manages resources.

Resilient Distributed Dataset (RDD) is the fundamental data structure in Spark providing fault-tolerant, distributed in-memory processing.

Spark supports Scala, Java, Python (PySpark), and R.

Hive is a data warehouse infrastructure built on Hadoop for providing data summarization, query, and analysis using a SQL-like interface (HiveQL).

Spark Streaming is a real-time data processing API for micro-batches; Structured Streaming is an improved, declarative API built on Spark SQL for continuous streaming.

ZooKeeper is a centralized service for maintaining configuration information, naming, synchronization, and group services.

HBase is a NoSQL database for random, real-time read/write access, while Hive provides batch-processing SQL-like querying and data analysis.

Partitioning divides tables into parts based on key column; bucketing divides data into more manageable files using a hash function on a column.

Authentication, authorization (Kerberos), data encryption, network security, and audit logging.

Directed Acyclic Graph (DAG) is Spark’s execution plan representing task dependencies.

HDFS replicates data blocks; MapReduce retries failed tasks; Spark uses lineage of RDDs for recomputation.

Narrow dependencies allow pipelined execution (one-to-one); wide dependencies require shuffle (many-to-many).

Data movement across executors that includes sorting and grouping, usually expensive in time and resources.

Adjust memory allocation, parallelism, replication factor, compression, and data locality.

Running duplicate tasks to handle stragglers and improve job completion time.

Ambari is a tool to provision, monitor, and manage Hadoop clusters.

Optional optimization function that runs on map output to reduce data transfer to reducers.

Using codecs like Snappy, LZO, and Zlib to reduce storage and network bandwidth.

Includes HDFS, YARN, MapReduce, Hive, Pig, HBase, Sqoop, Flume, and Oozie.

Spark uses Catalyst optimizer and Tungsten execution engine to optimize logical plans, physical plans, and efficiently manage memory and CPU.

Use Kerberos authentication, secure data encryption (at-rest and in-transit), role-based access control, and audit logging.

Data quality, security, privacy, compliance, and lifecycle management policies ensuring trust and proper use of data.

An architecture designed for processing massive quantities of data by combining batch and real-time streaming data processing systems.

MLlib is Spark’s scalable machine learning library providing algorithms and utilities for classification, regression, clustering, etc.

Big Data processing is distributed, scalable, deals with unstructured data, and often in near real-time, unlike traditional batch and structured processing.

A data pipeline is a series of data processing steps that ingest, transform, and move data from sources to storage and analysis systems.

Frameworks like Apache Kafka, Apache Flink, and Spark Streaming provide real-time processing of data streams.

CAP theorem asserts that distributed systems can only guarantee two of Consistency, Availability, and Partition Tolerance simultaneously; Big Data systems often prioritize availability and partition tolerance.

Using efficient data partitioning, caching, compression, tuning resource usage, and minimizing shuffles and joins in processing.

Containers enable consistent, portable, and scalable Big Data application deployment across environments.

Tracking the origin and changes of data throughout its lifecycle, helping with compliance and debugging.

ETL extracts, transforms, then loads data; ELT extracts, loads data, then transforms it inside the target system.

By designing flexible schemas, using schema registries, and supporting backward and forward compatibility.

Modern systems process both historical and real-time data streams using frameworks like Apache Flink or Spark Structured Streaming.

Making data accessible to all users in an organization to facilitate decision-making and innovation.

Challenges include data privacy, quality control, compliance with regulations, and consistency across diverse sources.

Big Data provides large-scale datasets, distributed processing, and model training/serving infrastructure for ML workloads.

Kafka is a distributed event streaming platform used for building real-time data pipelines and streaming apps.

Lambda uses separate batch and stream layers; Kappa processes data only as streams simplifying architecture.