Data storage systems

🧱 1. Operational Databases (OLTP Systems)

These are the classic, transactional databases used for daily operations — the ones we usually just call “databases.”

  • Purpose: Store and manage live data for apps or systems.

  • Data Type: Structured (tables, rows, columns).

  • Examples:

    • Relational: MySQL, PostgreSQL, Oracle, SQL Server

    • NoSQL (non-relational): MongoDB, Cassandra, Redis

Used for: e-commerce transactions, banking systems, CRM apps, etc.

🧠 2. Analytical Databases (OLAP Systems)

These include data warehouses — optimized for analysis and reporting, not transactions.

  • Examples: Snowflake, Amazon Redshift, Google BigQuery, Azure Synapse

🌊 3. Data Lakes

Hold raw or unstructured data (logs, images, sensor data, etc.).

  • Examples: Amazon S3 (as a lake), Azure Data Lake Storage, Google Cloud Storage

Now, here are other important types beyond those three 👇

🗂️ 4. Data Lakehouse

A hybrid between a data warehouse and a data lake.

  • Can handle structured + unstructured data.

  • Supports both analytics (like a warehouse) and machine learning (like a lake).

  • Examples: Databricks Lakehouse, Snowflake Unistore, Google BigLake

🕸️ 5. NoSQL Databases

Designed for flexibility, scalability, and speed — not limited to traditional tables.

  • Types of NoSQL:

    • Document-based: MongoDB, CouchDB

    • Key-value stores: Redis, DynamoDB

    • Column-family: Cassandra, HBase

    • Graph databases: Neo4j, Amazon Neptune

Used for apps that handle lots of changing, semi-structured data (like social media or IoT systems).

🧩 6. Time-Series Databases

Optimized for data that changes over time — like sensor readings or stock prices.

  • Examples: InfluxDB, TimescaleDB, Prometheus

🧭 7. Graph Databases

Store data as nodes and relationships, ideal for networks or connections.

  • Examples: Neo4j, ArangoDB, Amazon Neptune

  • Used for: Social networks, recommendation systems, fraud detection.

💬 8. Object & Document Stores

Used for storing multimedia, documents, or blobs (binary large objects).

  • Examples: Amazon S3, Google Cloud Storage, MongoDB (for JSON docs)

⚙️ 9. In-Memory Databases

Store data directly in RAM for extremely fast performance.

  • Examples: Redis, Memcached

  • Used for: Caching, real-time leaderboards, session data.

🏗️ Summary Table

CategoryPurposeData TypeExamples
Operational (OLTP)Real-time transactionsStructuredMySQL, PostgreSQL
Analytical (OLAP / Warehouse)Reporting & BIStructuredSnowflake, BigQuery
Data LakeRaw big dataAll typesAWS S3, Azure DL
Data LakehouseUnified analyticsAll typesDatabricks, Snowflake
NoSQLFlexible data modelsSemi/unstructuredMongoDB, Cassandra
Time-SeriesTime-based dataStructuredInfluxDB, TimescaleDB
GraphRelationship networksStructuredNeo4j, Neptune
In-MemoryUltra-fast accessStructuredRedis, Memcached

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