DBMS_7th_ch10

What is one of the motivations for Big Data?

• Driven by growth of web, social media, and internet-of-things

• Data is stored in a single machine

• Data volumes are very small

• Only relational data is handled

What are the three main characteristics that differentiate Big Data from earlier generation databases?

• Volume, Velocity, Variety

• Speed, Accuracy, Efficiency

• Relational, Non-relational, Hierarchical

• Structured, Unstructured, Semi-structured

What are the key components of Big Data storage systems?

• Parallel databases, Data coherency, Query processing systems

• Distributed file systems, Shardring, Key-value storage systems

• Transactional processing systems, Query processing systems, Distributed file systems

• Single file system view, Replicated storage, High scalability

What is the purpose of sharding in Big Data storage systems?

• To support non-relational data

• To provide a single file-system view

• To partition data across multiple databases

• To replicate data across multiple machines

Which system is an example of a distributed file system for large data-intensive applications?

• Google File System (GFS)

• Hadoop File System (HDFS)

• Apache Cassandra

• Amazon DynamoDB

What is the block size typically used in Hadoop File System (HDFS)?

• 256 MB

• 128 MB

• 64 MB

• 32 MB

What is the main advantage of using key-value storage systems?

• Support for transactional updates

• Support for joins and integrity constraints

• Efficient handling of relational data

• Ability to store large numbers of small records

Which system is an example of a document store for semi-structured data?

• Amazon S3

• MongoDB

• Apache HBase

• Google BigTable

What is the purpose of the MapReduce paradigm?

• To handle relational data efficiently

• To support transactional processing

• To provide a single file-system view

• To abstract issues of distributed and parallel environment

What is the main advantage of using a Combiner in MapReduce?

• Performs partial aggregation to minimize network traffic

• Handles data partitioning across multiple machines

• Provides a single file-system view

• Executes map and reduce functions in parallel