Data Storage and Hadoop Ecosystem Quiz

<p>To prevent data loss. (C)</p> Signup and view all the answers

<p>Removing incomplete rows, filling in missing values, or using imputation techniques. (C)</p> Signup and view all the answers

<p>To prevent distortion of analysis results. (C)</p> Signup and view all the answers

<p>To facilitate further processing and analysis. (D)</p> Signup and view all the answers

<p>Providing distributed data storage. (C)</p> Signup and view all the answers

<p>Hadoop MapReduce (D)</p> Signup and view all the answers

<p>Sqoop (A)</p> Signup and view all the answers

<p>Processing blocks of already stored data over a specific period. (C)</p> Signup and view all the answers

<p>Data warehousing and SQL-like querying. (C)</p> Signup and view all the answers

<p>Delivering a low-latency NoSQL database. (A)</p> Signup and view all the answers

<p>Download the software and its dependencies (A)</p> Signup and view all the answers

<p>Oozie (A)</p> Signup and view all the answers

<p>core-site.xml, hdfs-site.xml, mapred-site.xml (B)</p> Signup and view all the answers

<p>Flume (A)</p> Signup and view all the answers

<p>Spark (D)</p> Signup and view all the answers

<p>java --version (D)</p> Signup and view all the answers

<p>By adjusting system limits and kernel parameters (A)</p> Signup and view all the answers

<p>To enable machine learning algorithms. (B)</p> Signup and view all the answers

<p>Grafana (A)</p> Signup and view all the answers

<p>To ensure data protection, usability, and compliance with legal standards. (A)</p> Signup and view all the answers

<p>Defining access controls using role-based restrictions. (A)</p> Signup and view all the answers

<p>To comply with laws and regulations like GDPR and HIPAA, respecting user privacy. (A)</p> Signup and view all the answers

<p>To outline how data should be used, shared, and stored to prevent misuse. (C)</p> Signup and view all the answers

<p>Encryption, secure passwords, and regular audits. (D)</p> Signup and view all the answers

<p>To combine data from multiple sources into a cohesive, centralized format. (B)</p> Signup and view all the answers

<p>Establishing common data definitions for consistency across sources. (D)</p> Signup and view all the answers

<p>To facilitate data extraction, transformation, and loading into a central repository. (D)</p> Signup and view all the answers

<p>To update data regularly across systems, ensuring information remains accurate and up-to-date. (C)</p> Signup and view all the answers

<p>Supports scalable analytics and drives operational excellence. (D)</p> Signup and view all the answers

<p>To ensure stakeholders can act on accurate, timely information. (A)</p> Signup and view all the answers

<p>To serve as proactive alerts, informing users of significant data changes. (C)</p> Signup and view all the answers

<p>Provide structured, in-depth insights with data visualizations. (B)</p> Signup and view all the answers

<p>By consolidating and visualizing data in real-time. (D)</p> Signup and view all the answers

<p>By enabling users to monitor performance, identify patterns, and make informed decisions with agility. (A)</p> Signup and view all the answers

<p>By ensuring data integrity, compliance, and scalability. (D)</p> Signup and view all the answers

<p>Accelerating availability of integrated data for timely insights. (B)</p> Signup and view all the answers

<p>It breaks down silos between disparate data systems (B)</p> Signup and view all the answers

<p>By ensuring compliance and governance of data assets. (B)</p> Signup and view all the answers

Signup and view all the answers

The process of safely organizing and storing collected data.

Different options for data storage, such as databases and data lakes.

Scalable storage solutions accessible from anywhere, provided by services like AWS and Google Cloud.

The logical arrangement of data for easy access and analysis.

Providing duplicate copies of data to prevent loss.

The process of fixing errors and standardizing data for analysis.

Methods to manage gaps in data, such as removal or filling in values.

Policies and standards for managing data access, privacy, and security.

Systems to restrict data access based on user roles.

Guidelines to comply with laws like GDPR and HIPAA.

Guidelines on how data should be used, shared, and stored.

Measures like encryption and audits to protect data integrity.

Combining data from different sources into a unified format.

Tools used for extracting, transforming, and loading data.

Ensuring data is up-to-date across multiple systems.

Tools that help visualize data for analysis, like Power BI and Tableau.

A comprehensive view of operations and performance by integrating data systems.

Automating workflows to reduce manual effort and speed up data availability.

Making data available to the right people at the right time for informed decisions.

Proactive alerts that inform users about significant changes in data.

Structured documents that provide insights, trends, and key metrics.

Interactive visual displays that consolidate data in real-time for quick insights.

Using accurate and timely information to guide business choices.

Ensuring accuracy and trustworthiness of data throughout its lifecycle.

Adhering to regulations and standards related to data management.

The ability to grow and manage increased data loads effectively.

A framework that includes multiple tools for Big Data processing and management.

Hadoop Distributed File System; a storage system designed to hold large data sets across multiple machines.

A programming model for processing large datasets with a distributed algorithm on a cluster.

A NoSQL database that runs on top of HDFS and allows for real-time read/write access to big data.

An open-source framework for data processing that can handle batch and streaming data efficiently.

A data warehouse infrastructure built on top of HDFS that provides data summarization and query capabilities.

A workflow scheduler system to manage Hadoop jobs, allowing for complex job coordination.

A machine learning library for Hadoop, providing scalable implementations of distributed algorithms.

A tool designed for transferring data between Hadoop and relational databases.

A distributed service for efficiently collecting, aggregating, and moving large amounts of log data.

Settings that define system parameters for applications to use.

Fundamental concepts like IP addresses and communication protocols.

Processes that run in the background, like Linux services or Windows tasks.

Files that set the parameters for software, such as core-site.xml for Hadoop.

Adjusting settings to enhance software efficiency, like JVM settings for Java.