Hive supports ____ as a form of dynamic partitioning, which optimizes data storage based on query patterns.

  • Bucketing
  • Clustering
  • Compression
  • Indexing
Hive supports Bucketing as a form of dynamic partitioning. Bucketing involves dividing data into fixed-size files or buckets based on the column values, optimizing storage and improving query performance, especially for certain query patterns.

In Sqoop, what is the significance of the 'split-by' clause during data import?

  • Combining multiple columns
  • Defining the primary key for splitting
  • Filtering data based on conditions
  • Sorting data for better performance
The 'split-by' clause in Sqoop during data import is significant as it allows the user to define the primary key for splitting the data. This is crucial for parallel processing and efficient import of data into Hadoop.

How does Parquet optimize performance for complex data processing operations in Hadoop?

  • Columnar Storage
  • Compression
  • Replication
  • Shuffling
Parquet optimizes performance through columnar storage. It stores data column-wise instead of row-wise, allowing for better compression and efficient processing of specific columns during complex data processing operations. This reduces the I/O overhead and enhances query performance.

How does HDFS achieve fault tolerance?

  • Data Compression
  • Data Encryption
  • Data Replication
  • Data Shuffling
HDFS achieves fault tolerance through data replication. Each data block is replicated across multiple nodes in the Hadoop cluster. If a node or block becomes unavailable, the system can retrieve the data from its replicated copies, ensuring data reliability and availability.

How would you approach data modeling in HBase for a scenario requiring complex query capabilities?

  • Denormalization
  • Implement Composite Keys
  • Use of Secondary Indexes
  • Utilize HBase Coprocessors
In scenarios requiring complex query capabilities, utilizing composite keys in data modeling can be effective. Composite keys allow for hierarchical organization and efficient retrieval of data based on multiple criteria, enabling the execution of complex queries in HBase.

How does Kerberos help in preventing unauthorized access to Hadoop clusters?

  • Authentication
  • Authorization
  • Compression
  • Encryption
Kerberos in Hadoop provides authentication, ensuring that only authorized users can access the Hadoop cluster. It uses tickets to verify the identity of users and prevent unauthorized access, thus enhancing the security of the Hadoop environment.

In a custom MapReduce job, what determines the number of Mappers that will be executed?

  • Input Data Size
  • Number of Partitions
  • Number of Reducers
  • Output Data Size
The number of Mappers in a custom MapReduce job is primarily determined by the size of the input data. Each input split is processed by a separate Mapper, and the total number of Mappers is influenced by the size of the input data and the configured input split size.

In Hadoop administration, _____ is essential for balancing data and processing load across the cluster.

  • HDFS Balancer
  • Hadoop Daemon
  • MapReduce
  • YARN
In Hadoop administration, HDFS Balancer is essential for balancing data and processing load across the cluster. The HDFS Balancer utility redistributes data blocks across DataNodes to ensure uniform data distribution and prevent data imbalance.

In Impala, ____ is a mechanism that speeds up data retrieval operations.

  • Data Caching
  • Data Compression
  • Data Indexing
  • Data Sorting
In Impala, Data Caching is a mechanism that speeds up data retrieval operations. Caching involves storing frequently accessed data in memory, reducing the need to read from disk and improving query performance. It is particularly useful for repetitive queries on large datasets.

What is the role of ZooKeeper in maintaining high availability in a Hadoop cluster?

  • Coordination
  • Data Storage
  • Fault Tolerance
  • Job Execution
ZooKeeper plays a crucial role in maintaining high availability by providing coordination services. It helps in synchronizing distributed processes and managing configuration information, making it easier to handle failover scenarios and ensuring that the Hadoop cluster operates smoothly.