DSE - Data Science Big Data Technologies Questions and Answers — Questions and Answers
Question 1: In the context of the "3 V's" of Big Data, which "V" refers to the different forms of data, such as structured (e.g., database tables), semi-structured (e.g., JSON, XML), and unstructured (e.g., text, images, video)?
- Volume
- Variety (Correct answer)
- Velocity
- Veracity
Correct answer: Variety
The 'Variety' of Big Data refers to the heterogeneity of data types. This includes structured data from traditional databases, semi-structured data like JSON or XML files, and unstructured data such as text documents, videos, and images. Volume refers to the amount of data, Velocity to the speed of data generation, and Veracity to the data's quality and trustworthiness.
Question 2: A data engineering team is migrating a legacy data processing pipeline from Hadoop MapReduce to Apache Spark to improve performance for iterative machine learning algorithms. What is the primary architectural advantage of Spark that enables this significant speedup?
- Superior fault tolerance through data replication.
- In-memory data processing, which avoids slow disk I/O between steps. (Correct answer)
- A more efficient distributed file system than HDFS.
- Native support for the Python programming language.
Correct answer: In-memory data processing, which avoids slow disk I/O between steps.
Apache Spark's main performance advantage over traditional Hadoop MapReduce is its ability to perform in-memory processing. Spark can load data into a cluster's memory and query it repeatedly without writing intermediate results to disk. This significantly speeds up iterative algorithms and interactive data analysis, whereas MapReduce writes to disk after each map and reduce stage.
Question 3: What is the primary role of Apache YARN (Yet Another Resource Negotiator) within the Hadoop 2.x and later ecosystems?
- Storing large files by splitting them into blocks and distributing them across the cluster.
- Providing a SQL-like query interface for data stored in HDFS.
- Managing cluster resources (CPU, memory) and scheduling jobs for various processing frameworks. (Correct answer)
- Executing the core data processing logic in a parallel, distributed manner.
Correct answer: Managing cluster resources (CPU, memory) and scheduling jobs for various processing frameworks.
YARN's fundamental role is to decouple resource management from data processing. It acts as the cluster's operating system, managing resources like CPU and memory and scheduling tasks submitted by different applications (e.g., MapReduce, Spark, Tez). HDFS handles storage, Hive provides a SQL interface, and frameworks like MapReduce or Spark execute the processing logic.
Question 4: A large retail company wants to create a central repository to store vast amounts of raw, unstructured data from various sources, including social media feeds, web server logs, and IoT sensor data. The data will be used by data scientists for exploratory analysis without a predefined schema. Which of the following solutions is best suited for this requirement?
- Data Lake (Correct answer)
- Relational Data Warehouse
- In-memory Database
- OLTP Database
Correct answer: Data Lake
A Data Lake is designed to store massive amounts of raw data in its native format. It uses a 'schema-on-read' approach, which is ideal for unstructured and semi-structured data where the use case is not defined upfront. In contrast, a Data Warehouse requires a predefined 'schema-on-write', making it suitable for structured data and specific reporting tasks.
Question 5: Which of the following technologies is primarily designed as a distributed, fault-tolerant, and high-throughput messaging system used to ingest and publish real-time streams of data?
- Apache Spark
- HDFS
- Apache Hive
- Apache Kafka (Correct answer)
Correct answer: Apache Kafka
Apache Kafka is a distributed event streaming platform built to handle high-throughput, real-time data feeds. It acts as a publish-subscribe messaging system, decoupling data producers from data consumers, and is a cornerstone of modern streaming data pipelines. Spark is a processing engine, HDFS is for storage, and Hive is for querying.
Question 6: A social media application needs a database to store user profiles, posts, and the complex relationships between users (e.g., friendships, follows). Queries will frequently involve traversing these relationships, such as finding 'friends of friends.' Which type of NoSQL database is specifically designed to handle this type of data model and query pattern efficiently?
- Key-Value Store
- Document Database
- Graph Database (Correct answer)
- Column-Family Store
Correct answer: Graph Database
Graph databases are purpose-built to store and navigate relationships. They treat entities (like users and posts) as nodes and the connections between them (like 'friends' or 'follows') as edges. This structure makes traversing complex relationships extremely efficient, which is a key requirement for the described social media application.
DSE - Data Science Big Data Technologies Questions and Answers
In the context of the "3 V's" of Big Data, which "V" refers to the different forms of data, such as structured (e.g., database tables), semi-structured (e.g., JSON, XML), and unstructured (e.g., text, images, video)?