In HiveQL, which command is used to load data into a Hive table?
- COPY FROM
- IMPORT DATA
- INSERT INTO
- LOAD DATA
In HiveQL, the command used to load data into a Hive table is LOAD DATA. This command is used to copy data from an external table or a local file system into a Hive table, making the data accessible for querying and analysis.
For tuning a Hadoop cluster, adjusting ____ is essential for optimal use of cluster resources.
- Block Size
- Map Output Size
- NameNode Heap Size
- YARN Container Size
When tuning a Hadoop cluster, adjusting the YARN Container Size is essential for optimal use of cluster resources. Properly configuring the container size ensures efficient resource utilization and helps in avoiding resource contention among applications running on the cluster.
What feature of Apache Spark contributes to its high processing speed compared to traditional MapReduce in Hadoop?
- Data Compression
- Data Replication
- In-memory Processing
- Task Scheduling
Apache Spark's high processing speed is attributed to its in-memory processing feature. Unlike traditional MapReduce, Spark stores intermediate data in memory, reducing the need for time-consuming disk I/O operations and accelerating data processing.
What is the significance of Apache Sqoop in Hadoop data pipelines, especially when interacting with relational databases?
- It enables the import and export of data between Hadoop and relational databases
- It is a distributed storage system for Hadoop
- It optimizes Hadoop jobs for better performance
- It provides a query language for Hadoop
Apache Sqoop is significant in Hadoop data pipelines as it facilitates the import and export of data between Hadoop and relational databases. It streamlines the transfer of data, allowing seamless integration between Hadoop's distributed storage and traditional relational databases.
Parquet is known for its efficient storage format. What type of data structure does Parquet use to achieve this?
- Columnar
- JSON
- Row-based
- XML
Parquet uses a columnar storage format. Unlike row-based storage, where entire rows are stored together, Parquet organizes data column-wise. This approach enhances compression and facilitates more efficient query processing, making it suitable for analytics workloads.
Which feature of Apache Hive allows it to efficiently process and analyze large volumes of data?
- Bucketing
- Data Serialization
- Indexing
- Vectorization
Vectorization is a feature in Apache Hive that enables the processing of large volumes of data by performing operations on entire vectors of data at once. This can significantly improve query performance in Hive.
When setting up a Hadoop cluster, what is the primary role of the DataNode?
- Execute MapReduce jobs
- Manage the Namenode
- Store and manage actual data blocks
- Store and manage metadata
The primary role of a DataNode in Hadoop is to store and manage the actual data blocks. DataNodes are responsible for storing and retrieving data, and they communicate with the NameNode to report the health and availability of the data blocks they store.
Which tool in Hadoop is primarily used for importing data from relational databases into HDFS?
- HBase
- Hive
- Pig
- Sqoop
Sqoop is a tool in the Hadoop ecosystem specifically designed for efficiently transferring data between Hadoop and relational databases. It facilitates the import of data from databases such as MySQL, Oracle, and others into the Hadoop Distributed File System (HDFS) for further processing.
In Hadoop, ____ is used to configure the settings for various services in the cluster.
- Ambari
- HDFS
- MapReduce
- YARN
In Hadoop, Ambari is used to configure the settings for various services in the cluster. Ambari provides a web-based interface to manage, monitor, and configure Hadoop services, making it easier for administrators to handle cluster settings.
The SequenceFile format in Hadoop is particularly suited for ____.
- Avro Serialization
- Handling Large Text Files
- Sequential Data Access
- Storing Images
The SequenceFile format in Hadoop is particularly suited for sequential data access. It is optimized for storing large amounts of data in a serialized, binary format, making it efficient for applications that require sequential read and write access, such as MapReduce tasks.