In Hadoop, ____ is a key indicator of the cluster's ability to process data efficiently.

  • Data Locality
  • Data Replication
  • Fault Tolerance
  • Task Parallelism
Data Locality is a key indicator of the cluster's ability to process data efficiently in Hadoop. It refers to the practice of placing computation close to the data, reducing the need for data movement across the network. This enhances performance by maximizing the use of locally stored data.

Advanced disaster recovery in Hadoop may involve using ____ for cross-cluster replication.

  • DistCp
  • Flume
  • Kafka
  • Sqoop
Advanced disaster recovery in Hadoop may involve using DistCp (Distributed Copy) for cross-cluster replication. DistCp is a Hadoop tool specifically designed for efficiently copying large amounts of data between clusters. It can be employed to replicate data for disaster recovery purposes, ensuring data consistency across different Hadoop clusters.

Apache Hive is primarily used for which purpose in a Hadoop environment?

  • Data Ingestion
  • Data Processing
  • Data Storage
  • Data Visualization
Apache Hive is primarily used for data processing in a Hadoop environment. It provides a SQL-like interface to query and analyze large datasets stored in Hadoop. It translates SQL queries into MapReduce jobs, making it easier for analysts and data scientists to work with big data.

Sqoop's ____ tool allows exporting data from HDFS back to a relational database.

  • Connect
  • Export
  • Import
  • Transfer
Sqoop's Export tool is used to export data from HDFS back to a relational database. This tool is essential for moving data from Hadoop to a relational database for further analysis or reporting.

What makes Apache Flume highly suitable for event-driven data ingestion into Hadoop?

  • Extensibility
  • Fault Tolerance
  • Reliability
  • Scalability
Apache Flume is highly suitable for event-driven data ingestion into Hadoop due to its fault tolerance. It can reliably collect and transport large volumes of data, ensuring that data is not lost even in the presence of node failures or network issues.

When designing a Hadoop-based solution for high-speed data querying and analysis, which ecosystem component is crucial?

  • Apache Drill
  • Apache Impala
  • Apache Sqoop
  • Apache Tez
For high-speed data querying and analysis, Apache Impala is crucial. Impala provides low-latency SQL queries directly on Hadoop data, allowing for real-time analytics without the need for data movement. It is suitable for scenarios where rapid and interactive analysis of large datasets is required.

A ____ in Apache Flume specifies the movement of data from a source to a sink.

  • Channel
  • Configuration
  • Pipeline
  • Sink
A Configuration in Apache Flume specifies the movement of data from a source to a sink. It defines the settings and parameters for the Flume agents, allowing users to customize the behavior of the data flow within the Flume pipeline.

How does the Hadoop Federation feature contribute to disaster recovery and data management?

  • Enables Real-time Processing
  • Enhances Data Security
  • Improves Fault Tolerance
  • Optimizes Job Execution
The Hadoop Federation feature contributes to disaster recovery and data management by improving fault tolerance. Hadoop Federation allows the distribution of namespace across multiple NameNodes, reducing the risk of a single point of failure. In the event of a NameNode failure, other NameNodes can continue to operate, contributing to a more robust disaster recovery strategy.

____ are key to YARN's ability to support multiple processing models (like batch, interactive, streaming) on a single system.

  • ApplicationMaster
  • DataNodes
  • Resource Containers
  • Resource Pools
Resource Containers are key to YARN's ability to support multiple processing models on a single system. They encapsulate the allocated resources and are used to execute tasks across the cluster in a flexible and efficient manner.

Apache Hive organizes data into tables, where each table is associated with a ____ that defines the schema.

  • Data File
  • Data Partition
  • Hive Schema
  • Metastore
Apache Hive uses a Metastore to store the schema information for tables. The Metastore is a centralized repository that stores metadata, including table schemas, partition information, and storage location. This separation of metadata from data allows for better organization and management of data in Hive.