Introduction to Databases

<p>One-to-one and one-to-many (C)</p> Signup and view all the answers

<p>Rapid prototyping and small datasets (A)</p> Signup and view all the answers

<p>World Bank (C)</p> Signup and view all the answers

<p>Budget execution and public debt (A)</p> Signup and view all the answers

<p>Returns only rows with matching values in both tables (B)</p> Signup and view all the answers

<p>Returns all rows from the left table and matched rows from the right table (C)</p> Signup and view all the answers

<p>In a single table or sheet (A)</p> Signup and view all the answers

<p>To summarize data and derive insights (C)</p> Signup and view all the answers

<p>LIKE (C)</p> Signup and view all the answers

<p>Adding columns or changing the structure of the table (D)</p> Signup and view all the answers

<p>Relational (B)</p> Signup and view all the answers

<p>All rows from the right table and matched rows from the left table are returned (B)</p> Signup and view all the answers

<p>To manipulate and transform text data (C)</p> Signup and view all the answers

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

<p>It specifies tables, columns, and relationships for a specific DBMS. (B)</p> Signup and view all the answers

<p>It uniquely identifies each row in a table. (C)</p> Signup and view all the answers

<p>It is a column that establishes a link to a primary key in another table. (B)</p> Signup and view all the answers

<p>Schema-less or flexible schema (B)</p> Signup and view all the answers

<p>To facilitate informed decision making and data analysis. (B)</p> Signup and view all the answers

<p>Physical Design (C)</p> Signup and view all the answers

<p>When flexibility and scalability are necessary (B)</p> Signup and view all the answers

<p>Rows and columns representing data entries. (D)</p> Signup and view all the answers

<p>Documents within the same collection can have different fields or structures. (A)</p> Signup and view all the answers

<p>To establish links between tables for data integrity and complex queries. (B)</p> Signup and view all the answers

<p>Caching mechanisms and session management (C)</p> Signup and view all the answers

<p>They represent data as nodes and relationships. (A)</p> Signup and view all the answers

<p>MySQL (C)</p> Signup and view all the answers

<p>Managed cloud service for Neo4j databases (D)</p> Signup and view all the answers

<p>Tracking relationships in social networks (A)</p> Signup and view all the answers

<p>To ensure consistency and accuracy of data (C)</p> Signup and view all the answers

<p>To assist in identifying entities and their relationships (B)</p> Signup and view all the answers

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

<p>Diamond (C)</p> Signup and view all the answers

<p>Developing applications and managing local Neo4j databases (B)</p> Signup and view all the answers

<p>A tool for natural language search and interactive visualizations (B)</p> Signup and view all the answers

<p>Improved data integrity (D)</p> Signup and view all the answers

<p>80% (D)</p> Signup and view all the answers

<p>Identify Entities (C)</p> Signup and view all the answers

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

<p>Hadoop Distributed File System (HDFS) (A)</p> Signup and view all the answers

<p>One to Many (C)</p> Signup and view all the answers

<p>175 ZB (D)</p> Signup and view all the answers

<p>Less than 20% (B)</p> Signup and view all the answers

<p>Community detection and centrality analysis (B)</p> Signup and view all the answers

<p>To handle large volumes of data across multiple servers (A)</p> Signup and view all the answers

Retrieving data from a table.

Adding new data to a table.

Modifying existing data in a table.

Removing data from a table.

Adding new columns to an existing table.

Removing a table entirely.

Combining rows from multiple tables based on related columns.

SQL operators are used for various operations on data like arithmetic calculations, comparisons, and logical combinations.

The most abstract level of database design, focused on defining entities and relationships without specifying implementation details.

A detailed level of database design specifying tables, columns, and relationships, ready for implementation in a specific database system.

The lowest level of database design specifying the physical storage of data, including file organization, indexing, and security measures.

A database that organizes data into related tables, enabling efficient storage and retrieval of information.

A unique identifier for each row in a table, ensuring data integrity by preventing duplicate records.

A column in one table that refers to a primary key in another table, establishing links between related data.

The importance of database design in economic and financial analysis is its ability to organize vast amounts of data for analysis and reporting.

A table in a relational database containing rows (records) and columns (fields) representing a specific entity and its attributes.

A database where data is stored in a single table, without complex relational structures.

The process of connecting tables in Excel to analyze related data from multiple sources within the same workbook.

A feature in Excel that allows users to create relationships between tables by identifying common fields.

A type of relationship in Excel where one record in the first table is associated with only one record in the second table.

A type of relationship in Excel where one record in the first table can be associated with multiple records in the second table.

A type of relationship not directly supported in Excel, where one record in the first table can be associated with multiple records in the second table and vice versa.

A table used to connect two tables with a many-to-many relationship in Excel.

Ensures data consistency and accuracy by preventing invalid data insertion. For example, a foreign key constraint prevents an order from referencing a non-existent customer.

Allows efficient retrieval of related data across multiple tables. For instance, joining Customers and Orders tables enables finding out which customer placed which order.

Eliminates the need to duplicate data. Related data is stored in separate tables to avoid redundancy. For example, customer details are stored once in the Customers table and referenced in the Orders table.

A visual representation of entities, their attributes, and relationships in a database. It helps with entity identification, relationship definition, normalization, and database implementation.

A visual element in an ERD that represents a real-world object, such as a customer, order, or product.

A visual element in an ERD that represents a connection between entities, indicating how they interact with each other.

The number of instances of one entity that can be associated with instances of another entity through a relationship. Types include one-to-one, one-to-many, many-to-one, and many-to-many.

A visual element in an ERD that represents a characteristic of an entity. It describes the entity's properties.

A type of database that does not enforce a strict schema, allowing for flexible data structures and quick changes.

The ability to increase processing power by adding more resources, typically servers or nodes, to a system.

A database designed to handle vast quantities of data and complex relationships, often used for analytics and real-time applications.

A type of non-relational database that stores data in collections of documents, each with its own structure and fields.

A database that stores data as key-value pairs, where the keys are unique identifiers and the values are associated data.

A database designed for storing and querying data with complex relationships, represented as nodes and edges.

A cloud service that provides a managed environment for deploying and running Neo4j graph databases.

The core product of Neo4j, a graph database management system available in Community and Enterprise editions.

A desktop application used for building and managing Neo4j graph databases, including visual exploration of graph data.

A tool designed for visual analysis of graph data, allowing users to explore connections and insights using natural language queries and interactive visualizations.

A library containing algorithms and tools specifically built for working with graph data, offering capabilities for tasks like centrality analysis, community detection, and more.

A fully managed graph database service hosted on Google Cloud Platform, designed for scalability and reliability.

The massive amount of data generated and stored globally, reaching an estimated 175 zettabytes by 2025.

A distributed file system designed to store massive amounts of data across multiple servers, providing high redundancy for data reliability.

Data that doesn't have a predefined structure, like text, images, and videos, making up the majority of global data.