Introduction

In this section we cover databases in the broadest of senses as any organized persistence systems. Let’s take a look at some od the database types we have. This does not include all the options but the most highly relevant ones at the time.

Relational Databases (SQL-Based)

Examples: MySQL, PostgreSQL, SQLite, Oracle DB

Pros:

• ACID compliance ensures reliability.
• Strong querying capabilities via SQL.
• Widely supported and documented.

Cons:

• Rigid schema design.
• Can struggle with unstructured or semi-structured data.

NoSQL Databases

Document Stores

Examples: MongoDB, CouchDB

Pros:

• Flexible schema.
• Suited for hierarchical or semi-structured data.

Cons:

• Lacks complex joins.
• Limited transaction support (in some implementations).

Key-Value Stores

Examples: Redis, DynamoDB

Pros:

• Extremely fast and lightweight.
• Ideal for caching or simple data.

Cons:

• Limited querying capability.
• Poor fit for complex data models.

Columnar Databases

Examples: Cassandra, HBase

Pros:

• Optimized for analytical workloads.
• Scales well horizontally.

Cons:

• Inefficient for transactional queries.
• Complex consistency management.

Graph Databases

Examples: Neo4j, Amazon Neptune

Pros:

• Ideal for relationship-heavy data.
• Intuitive for queries on networks/graphs.

Cons:

• Specialized use case.
• Not as performant for traditional queries.

Time-Series Databases

Examples: InfluxDB, TimescaleDB

Pros:

• Optimized for time-series data (e.g., metrics, logs).
• Efficient storage and retrieval for sequential data.

Cons:

• Limited general-purpose capabilities.
• Specialized query syntax.

Search Engines (Database-like)

Examples: Elasticsearch, Solr

Pros:

• Powerful full-text search.
• Real-time analytics capabilities.

Cons:

• High resource usage.
• Limited transactional support.

In-Memory Databases (Caches)

Examples: Redis, Memcached

Pros:

• Ultra-fast performance.
• Ideal for caching and ephemeral data.

Cons:

• Volatile unless explicitly persisted.
• Storage constrained by RAM.

Messaging and Event Streaming Systems

Comparison to Traditional Databases:

Unlike traditional databases, these systems focus on managing data in motion rather than at rest.

Use Cases: Suitable for real-time data pipelines, event-driven architectures, and applications where temporal ordering is key ( e.g., IoT, transaction logging, monitoring).

Message Brokers

Examples: RabbitMQ, ActiveMQ

Pros:

• Reliable message delivery (supports persistence).
• Good for decoupling microservices.
• Supports various messaging patterns (e.g., pub/sub, point-to-point).

Cons:

• Not ideal for real-time analytics.
• Limited scalability compared to distributed log systems.

Distributed Log Systems / Event Streaming Platforms

Examples: Kafka, Pulsar

Pros:

• High throughput and fault tolerance.
• Retains event history for replayability.
• Excellent for real-time processing pipelines.

Cons:

• More complex to set up and manage.
• Not a general-purpose database; optimized for logs/events.

Streaming Data Processors

Examples: Apache Flink, Apache Storm

Pros:

• Real-time computation on event streams.
• Well-suited for ETL and continuous analytics.

Cons:

• Requires additional integration for persistence.
• Higher learning curve.