Section 1.1: Relational Databases

Concept

The relational database model, introduced by Edgar F. Codd in 1970, is founded on relational algebra principles. Data is organized in tables consisting of rows and columns; each row signifies a record and each column an attribute. Relationships among tables are maintained using primary and foreign keys.

Best Use Case

These databases are adept at managing structured and tabular data with clearly defined relationships, making them ideal for applications necessitating data integrity, ACID (Atomicity, Consistency, Isolation, Durability) transactions, and complex queries.

Current Vendors

Sample Data

The data representation is typically tabular, as shown in the example from Learn MySQL, which demonstrates the use of the INSERT statement.

Section 1.2: Columnar Databases

Concept

Columnar databases organize data in columns instead of rows, optimizing data retrieval and analysis, particularly for analytical tasks. This structure facilitates better compression and efficient querying of specific columns.

Best Use Case

These databases are suited for analytics and business intelligence applications where large volumes of data need to be aggregated and analyzed, excelling in read-heavy environments.

Current Vendors

Sample Data

While visually similar to relational databases, the underlying storage differs, as noted in Google's tutorial on querying data in BigQuery.

Section 1.3: Document Databases

Concept

Document databases maintain data in semi-structured documents, typically formatted in JSON or BSON. Each document comprises key-value pairs and can vary in structure, allowing for easy schema evolution.

Best Use Case

These databases are ideal for projects with frequently changing data models where flexibility is essential, commonly utilized in content management systems, real-time analytics, and mobile apps.

Current Vendors

Sample Data

For example, a blog post in a document database can be illustrated as a JSON document:

{

"_id": 1,

"title": "Introduction to Document Databases",

"content": "Document databases are NoSQL databases…",

"author": "John Doe",

"tags": ["databases", "NoSQL", "MongoDB"],

"date": "2023–07–01"

}

Section 1.4: Graph Databases

Concept

Graph databases utilize graph structures to represent and store data, featuring nodes (entities) connected by edges (relationships). This configuration allows for efficient navigation of complex relationships, making it particularly beneficial for interconnected data scenarios.

Best Use Case

These databases are exceptional for applications that depend on intricate relationships and complex querying, such as social networks, recommendation systems, and fraud detection.

Current Vendors

Sample Data

Graph data can be effectively visualized, as demonstrated in the AuraDB example using Neo4j.

Section 1.5: Key-Value Databases

Concept

Key-Value databases organize data as pairs of keys and values, where each key uniquely identifies a value. This straightforward structure makes them highly efficient for rapid retrieval and storage of extensive data.

Best Use Case

These databases are perfect for scenarios where quick read/write access to individual records is essential, such as caching, session management, and real-time applications.

Current Vendors

Sample Data

For instance, a key-value database could be used to manage user sessions in a web application, as shown below:

Key: session_12345

Value: { "user_id": 9876, "expires": "2023–07–31 12:00:00" }

Section 1.6: Time-Series Databases

Concept

Time-Series databases are designed specifically for time-stamped data, where each data point is associated with a timestamp. They are optimized for efficient storage, retrieval, and analysis of data that is organized by time.

Best Use Case

These databases are critical for applications that involve monitoring, IoT, financial data, or any area that requires tracking and analyzing events over time.

Current Vendors

Sample Data

Consider a time-series database recording temperature data from IoT sensors; it resembles columnar or relational databases but is specifically optimized for its purpose. Scenarios using Amazon Timestream with Grafana for log monitoring illustrate its capabilities.