データベースの種類と各々の特徴が記載されている
種類はRDBMS・NoSQL・NewSQL・Time-Series・In-Memory
最新のNewSQLもあり、日々身につけるだけで覚えていく。
街ゆく同士へ同業であるというアピールできるプロトコルTシャツとも呼べるであろう。

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■ Relational Database Management System

・Data consistency & integrity: Ensure that data remains consistent and accurate across the database.
・Scalability: Scale vertically by adding more resources to a single server, and horizontal scaling through sharding or partitioning.
・Flexibility: Handle various data types and structures, and can be customized to meet specific business needs.
・ACID properties: Transactions are Atomic, Consistent, Isolated, and Durable, providing a reliable foundation for data management.

■ NoSQL Database

・Document Store: Stores and retrieves data as semi-structured documents, typically using formats like JSON or BSON.
・Key-Value Store: Stores data as pairs of unique keys and their corresponding values, enabling fast and efficient lookups.
・Column Store: Stores data in columns instead of rows, optimizing for read-heavy workloads and allowing efficient compression and querying.
・Graph Database: Designed for storing and querying complex relationships and interconnected data using nodes and edges in a graph structure.

■ NewSQL Database

Designed to provide the ACID properties and SQL support of RDBMS,
while offering the scalability and performance advantages of NoSQL systems.

・ACID compliance: Ensure data consistency and reliability by adhering to ACID properties for transactions.
・SQL support: Providing a familiar interface for developers and database.
・Horizontal scalability: By adding more nodes, enabling them to handle increasing amounts of data and transaction loads efficiently.
・Distributed architecture: Use distributed consensus algorithms, data sharding, and replication techniques.

■ Time-Series Database

Efficiently store, retrieve, and analyze large volumes of time-stamped data with high write and query rates.
Its unique architecture and indexing mechanisms enable fast data ingestion and time-based querying,
making it ideal for handling time-sensitive data and performing time-based analytics.

・Time-based indexing: Allowing for efficient querying and aggregation of data over specified time intervals.
・compression: Reduce storage overhead and improve query performance, as time-series data can be highly repetitive.
・Retention policies: Automatically expire or downsample older data, optimizing storage and resource usage.

■ In-Memory Database

Designed to handle high-performance, low-latency workloads, such as real-time analytics, caching,
and high-speed transaction processing.

・High performance: By storing data in memory, IMDBs can offer significantly faster read and write operations compared to disk-based databases.
・Real-time analytics: Real-time data processing and analysis, making them suitable for applications that require immediate insights.
・Scalability: In-Memory Databases can scale horizontally and vertically to accommodate increased data volumes and processing demands.
・Data persistence: Optional disk-based storage for data persistence and recovery, ensuring data durability in case of system failures or restarts.

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