Question 1

What is Data Science?

Accepted Answer

Data Science is the highly multidisciplinary mathematical field combining statistics, advanced algorithms, and machine learning architectures to violently extract hidden, actionable intelligence from massive datasets.

Question 2

Define unstructured data.

Accepted Answer

Unstructured data is chaotic, raw information completely lacking a predefined mathematical data model or rigid schema, rendering it unreadable to standard relational SQL databases.

Question 3

What was the role of the JobTracker in Hadoop 1.x?

Accepted Answer

In the primitive Hadoop 1.x architecture, the JobTracker was the absolute centralized master node mathematically responsible for both cluster resource management and MapReduce job scheduling.

Question 4

What is Apache Oozie?

Accepted Answer

Apache Oozie is a massive, server-based workflow scheduling architecture mathematically designed strictly to manage and orchestrate complex, dependent Hadoop MapReduce and Pig/Hive jobs.

Question 5

Give a use case for Spark SQL.

Accepted Answer

Spark SQL is utilized to execute mathematically massive, real-time interactive queries over Petabytes of JSON data stored in an HDFS Data Lake using standard SQL syntax.

Question 6

What are column-family databases?

Accepted Answer

Column-family databases (like Apache Cassandra or HBase) are massive NoSQL architectures that physically store data strictly by columns rather than traditional SQL rows.

Question 7

What is Apache Sqoop?

Accepted Answer

Apache Sqoop is a highly aggressive command-line architecture mathematically engineered strictly to violently transfer massive volumes of data between relational databases (MySQL) and Hadoop (HDFS).

Question 8

Define regression in data analytics.

Accepted Answer

Regression is a predictive mathematical algorithm in machine learning that models the strict statistical relationship between independent variables (features) and a continuous dependent variable (target).

Question 9

Name a popular graph database.

Accepted Answer

Neo4j is the absolute industry-standard open-source Graph Database, mathematically engineered to violently map complex, highly connected data topologies.

Question 10

What is a Resilient Distributed Dataset (RDD)?

Accepted Answer

An RDD is the absolute fundamental mathematical data structure of Apache Spark: an immutable, partitioned collection of records distributed across massive server clusters.

Question 11

Describe a typical Data processing pipeline in Big Data.

Accepted Answer

An architectural breakdown of the Big Data Processing Pipeline. Violently details the strict physical sequence of Data Ingestion (Kafka), Storage (HDFS), Processing (Spark), and Visualization, exposing how massive data flows from edge to insight.

Question 12

What are the key differences between Hadoop 1.x and Hadoop 2.x?

Accepted Answer

A critical architectural comparison between Hadoop 1.x and Hadoop 2.x. Details the catastrophic bottleneck of the monolithic JobTracker and how the invention of YARN violently decentralized cluster resource management, enabling real-time processing beyond MapReduce.

Question 13

How does Sqoop work?

Accepted Answer

A definitive explanation of Apache Sqoop's underlying architecture. Details how it completely bypasses traditional slow ETL by mathematically compiling Java code and launching massive parallel Map-Only jobs to violently extract data from SQL servers into HDFS.

Question 14

Explain Hive partitioning and bucketing.

Accepted Answer

A critical mathematical breakdown of Apache Hive performance tuning. Details how Partitioning physically isolates data into rigid directory structures, while Bucketing uses modulo hash functions to violently cluster data for explosive JOIN performance.

Question 15

Compare Spark processing with Hadoop MapReduce.

Accepted Answer

A definitive architectural contrast between Apache Spark and Hadoop MapReduce. Violently details the catastrophic disk I/O bottleneck of MapReduce against Spark's explosive in-memory RDD architecture and Directed Acyclic Graph (DAG) execution.

Question 16

Explain the concept of Decision trees in big data ML.

Accepted Answer

A rigorous mathematical exposition of Decision Trees in Machine Learning. Details how the algorithm recursively partitions massive datasets using Information Gain and Entropy mathematics to construct a rigid, interpretable predictive architecture.

Question 17

What are Recommender systems?

Accepted Answer

A critical review of Recommender Systems. Explains how Netflix and Amazon utilize massive Big Data architectures to mathematically predict user preferences through Content-Based filtering and the highly complex matrix factorization of Collaborative Filtering.

Question 18

Discuss the complete Big Data Analytics lifecycle.

Accepted Answer

A massive architectural breakdown of the Big Data Analytics Lifecycle. Violently dissects the strict 6-phase algorithmic process from initial Business Discovery to mathematical Model Building, Operationalization, and final strategic delivery.

Question 19

Explain the Lambda architecture for big data systems.

Accepted Answer

An exhaustive mathematical dissection of the Lambda Architecture. Details how Nathan Marz violently solved the conflict between slow batch processing (Hadoop) and inaccurate real-time streaming (Storm) by fusing them into an impenetrable, immutable three-layer system.

Question 20

Detail real-time analytics using Kafka and Spark.

Accepted Answer

A definitive architectural guide to Real-Time Analytics using Apache Kafka and Spark Streaming. Explains how Kafka's massive distributed log buffers the chaotic data tsunami, and how Spark's Micro-Batching mathematics violently processes the stream in sub-second intervals.

Question 21

Describe the Pig execution environment and its main components.

Accepted Answer

An exhaustive theoretical breakdown of Apache Pig. Details how the Pig Latin dataflow language completely abstracts the horrific complexity of raw Java MapReduce, aggressively translating high-level relational operations into optimized mathematical DAGs.

Question 22

Explain Collaborative filtering in Recommender Systems.

Accepted Answer

A massive mathematical analysis of Collaborative Filtering in Recommender Systems. Violently dissects User-Based vs. Item-Based approaches, detailing the catastrophic cold start problem and the elite execution of Matrix Factorization via Singular Value Decomposition (SVD).