For real-time data syncing between Hadoop and RDBMS, Sqoop can be integrated with ____.
- Apache Flink
- Apache HBase
- Apache Kafka
- Apache Storm
For real-time data syncing between Hadoop and RDBMS, Sqoop can be integrated with Apache Kafka. Kafka enables the seamless and real-time transfer of data between Hadoop and relational databases, supporting continuous data integration.
For ensuring high availability in Hadoop, an administrator must configure ____ effectively.
- Data Compression
- Job Scheduling
- NameNode HA
- Rack Awareness
For ensuring high availability in Hadoop, an administrator must configure NameNode High Availability (NameNode HA) effectively. This involves setting up multiple NameNodes and ensuring seamless failover in case of a NameNode failure, enhancing the reliability of the Hadoop cluster.
How can a Hadoop administrator identify and handle a 'Small Files Problem'?
- CombineFileInputFormat
- Data Aggregation
- Hadoop Archive
- SequenceFile Compression
To address the 'Small Files Problem,' a Hadoop administrator can use CombineFileInputFormat. This technique allows the efficient processing of small files by combining them into larger input splits, reducing the overhead associated with managing numerous small files and improving overall processing efficiency.
____ is used to estimate the processing capacity required for a Hadoop cluster based on data processing needs.
- Capacity Planning
- HDFS
- MapReduce
- YARN
Capacity Planning is used to estimate the processing capacity required for a Hadoop cluster based on data processing needs. It involves analyzing factors like data volume, processing speed, and storage requirements to ensure optimal cluster performance.
How does Hadoop's ResourceManager assist in monitoring cluster performance?
- Data Encryption
- Node Health Monitoring
- Resource Allocation
- Task Scheduling
Hadoop's ResourceManager is responsible for resource allocation and management in the cluster. It assists in monitoring cluster performance by efficiently allocating resources to applications, ensuring optimal utilization and performance. This includes managing memory, CPU, and other resources for running tasks.
Apache Spark improves upon the MapReduce model by performing computations in _____.
- Cycles
- Disk Storage
- In-memory
- Stages
Apache Spark performs computations in-memory, which is a key improvement over the MapReduce model. This in-memory processing reduces the need for intermediate disk storage, resulting in faster data processing and analysis.
Impala's ____ feature allows it to process and analyze data stored in Hadoop clusters in real-time.
- Data Serialization
- In-memory
- MPP
- SQL-on-Hadoop
Impala's in-memory processing feature enables it to store and analyze data in memory, providing faster query performance and real-time data analysis capabilities in Hadoop clusters.
_____ is a critical factor in Hadoop Streaming API when dealing with streaming data from various sources.
- Data Aggregation
- Data Partitioning
- Data Replication
- Data Serialization
Data Serialization is a critical factor in Hadoop Streaming API when dealing with streaming data from various sources. Proper serialization ensures that the data is efficiently encoded and decoded, enhancing the performance of data processing in Hadoop Streaming.
How does Apache Flume facilitate building data pipelines in Hadoop?
- It enables the orchestration of MapReduce jobs
- It is a data ingestion tool for efficiently collecting, aggregating, and moving large amounts of log data
- It is a machine learning library for Hadoop
- It provides a distributed storage system
Apache Flume facilitates building data pipelines in Hadoop by serving as a reliable and scalable data ingestion tool. It efficiently collects, aggregates, and moves large amounts of log data from various sources to Hadoop storage, making it a valuable component in data pipeline construction.
In a Hadoop cluster, what is the primary role of DataNodes?
- Coordinate resource allocation
- Execute MapReduce jobs
- Manage metadata
- Store and manage data blocks
The primary role of DataNodes in a Hadoop cluster is to store and manage data blocks. They are responsible for storing the actual data and are distributed across the cluster to ensure fault tolerance and parallel data processing. DataNodes report to the NameNode about the health and status of the data blocks they store.