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https://github.com/chaseofthejungle/types-of-databases

An overview of 9 different types of databases, and use cases for all of them.
https://github.com/chaseofthejungle/types-of-databases

database-types nosql-databases rdbms relational-databases

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An overview of 9 different types of databases, and use cases for all of them.

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# Overview of Database Types

  

**Overview:** This guide covers 9 popular database types (*note: DB solutions can cover multiple categories, and the following is not an exhaustive list of database solutions and how many categories they overlap in*):



#### Table of Contents:



1. [Popular Database Types (with examples)](#popular-db)

2. [Database Use Cases](#use-cases)

3. [Scaling Features](#scaling)

4. [Supplemental Resources](#supplemental)

  



  

## 1. Popular Database Types (with examples)

  

* **Columnar Databases**

    - [Amazon Redshift](https://aws.amazon.com/redshift/): Intended for big data queries and analytics.

    - [Apache Cassandra](https://cassandra.apache.org/_/index.html): Handles massive data volumes with high availability and fault tolerance. Intended for high speed settings (e.g., e-commerce, CMSes, social media).

    - [Apache HBase](https://hbase.apache.org/): Used to handle big data, social media, and IoT apps, with dashboards for real-time analytics.

    - [ClickHouse](https://clickhouse.com/): Popular for online analytical processing in large data settings, with dashboard-accessible insights.

    - [Google Bigtable](https://cloud.google.com/bigtable): Processes financial, marketing, and time-series data.

* **Document-Based Databases**

    - [Amazon DocumentDB](https://aws.amazon.com/documentdb/): Enables quick scaling of memory and computational resources for Amazon's Virtual Private Cloud.

    - [Apache CouchDB](https://couchdb.apache.org/): Can operate database servers via virtual machines and other servers.

    - [MongoDB](https://www.mongodb.com/): Builds highly scalable and available web apps. Utilized by MERN and MEAN Stack Developers.

    - [RavenDB](https://ravendb.net/): Includes a cloud service for handling administrative tasks (e.g., user security, hardware installations/maintenance).

    - [RethinkDB](https://rethinkdb.com/): Useful for tracking real-time changes and applying block/non-block requests.

* **Graph-Based Databases**

    - [Amazon Neptune](https://aws.amazon.com/neptune/): Builds and executes apps that handle connected datasets (e.g., network security, fraud detection, recommendation engine apps).

    - [ArangoDB](https://arangodb.com/): Enables data integration for relationship and activity analysis. A popular solution for fraud monitoring/detection apps.

    - [Azure Cosmos DB](https://azure.microsoft.com/en-us/products/cosmos-db): Builds AI-enabled, web, mobile, and other apps that handle large quantities of data.

    - [Neo4j](https://neo4j.com/): Detects data patterns and provides data persistence and querying for complex relationships.

    - [OrientDB](https://orientdb.org/): Utilized for big data processing while providing scalability and flexibility. A popular solution for social networks, banks, and fraud prevention/detection systems.

* **In-Memory Databases**

    - [Memcached](https://memcached.org/): Supports database load balancing and increases speed/processing efficiency of web apps.

    - [Microsoft Azure (OLTP)](https://learn.microsoft.com/en-us/azure/architecture/data-guide/relational-data/online-transaction-processing): Records real-time interactions and allows the data to be queried for quick decision making.

    - [Redis](https://redis.io/): Can be utilized dynamically (as a cache, database, or message broker).

    - [SAP HANA](https://www.sap.com/products/data-cloud/hana/what-is-sap-hana.html): Utilized for diverse operations (e.g., advanced analytics, custom app development and deployment, managing processes).

    - [Volt Active Data](https://www.voltactivedata.com/): Empowers enterprises with high scalability, without compromising data or data processing.

* **NoSQL Databases**

    - [Actian NoSQL Object Database](https://www.actian.com/databases/nosql/): Provides transactional storage of data via object-oriented programming languages, which serve as data definition languages.

    - [Apache Cassandra](https://cassandra.apache.org/_/index.html):  Handles massive data volumes with high availability and fault tolerance. Intended for high speed settings (e.g., e-commerce, CMSes, social media).

    - [Apache HBase](https://hbase.apache.org/): Used to handle big data, social media, and IoT apps, with dashboards for real-time analytics.

    - [Couchbase](https://www.couchbase.com/): Manages sessions, provides user profiles in real-time, and can store data for offline operations via mobile apps.

    - [MongoDB](https://www.mongodb.com/): Builds highly scalable and available web apps. Utilized by MERN and MEAN Stack Developers.

    - [Redis](https://redis.io/): Can be utilized dynamically (as a cache, database, or message broker).

* **Object-Oriented Databases**

    - [Actian NoSQL Object Database](https://www.actian.com/databases/nosql/): Provides transactional storage of data via object-oriented programming languages, which serve as data definition languages.

    - [Actian db4o (Legacy Technology)](https://dbdb.io/db/db4o): Intended for .NET and Java developers. Discontinued since October 2014.

    - [GemStone/S](https://gemtalksystems.com/products/gs64/): Supports mission-critical apps, integrating the Smalltalk programing language for development, deployment, and management.

    - [ObjectDB](https://www.objectdb.com/): Relies on two Java APIs (JDO and JPA) instead of utilizing a proprietary API and implementing an ORM as an intermediary.

    - [ZODB](https://zodb.org/en/latest/): Provides data persistence and transparency for Python objects.

* **Relational Databases**

    - [CockroachDB](https://www.cockroachlabs.com/): Popular for cloud-based software-as-a-service apps, financial systems, Kubernetes (k8s) services, OLTP, and transactional platform solutions.

    - [Microsoft SQL Server](https://www.microsoft.com/en-us/sql-server): Provides data persistence and data fetching for diverse workloads, including client-based and cloud-based apps.

    - [Oracle MySQL](https://www.mysql.com/): Utilized by content management systems (such as for academic purposes), e-commerce stores, embedded systems, and web apps.

    - [PostgreSQL](https://www.postgresql.org/): A highly portable database solution with an active international support community. Capable of handling diverse workloads and providing high availability even when many concurrent users are present.

    - [SQLite](https://www.sqlite.org/): An embedded database solution, rather than a standalone app. A popular choice for embedded systems, operating systems, smart phones, and web browsers. 

* **Time-Oriented Databases**

    - [Apache Druid](https://druid.apache.org/)

    - [InfluxDB](https://www.influxdata.com/)

    - [OpenTSDB](https://opentsdb.net/)

    - [Prometheus](https://prometheus.io/)

    - [TimescaleDB](https://www.timescale.com/)

* **Vector-Based Databases**

    - [Chroma](https://www.trychroma.com/)

    - [Milvus](https://milvus.io/)

    - [Pinecone](https://www.pinecone.io/)

    - [Qdrant](https://qdrant.tech/)

    - [Weaviate](https://weaviate.io/)

  




## 2. Database Use Cases

  

Before diving into specific use cases for each type of database, several factors should be considered. These include: cost of operations (can the organization cover it, and is there a more efficacious alternative?), employee/team expertise (will it take significant training to implement the database solution?), whether it makes sense to host services in-house or via a cloud provider, whether scalability is a significant concern (such as it is for streaming services and some embedded/IoT environments), and if one's organization is prepared to back up and recover data, or install and maintain backup/recovery solutions corresponding to the selected database solution.

  

*Use cases for these nine database types include...*

  

| Database Type | Use Cases |

| ------- | ------- |

| **Columnar** | Business intelligence (BI), data warehousing, significant analytics and enterprise-level reporting needs. |

| **Document Based** | Content management, JSON and JSON-like data, fast development deadlines, need for flexibile schematics. |

| **Graph Based** | Social bookmarking, networking, and recommendation-based sites, interconnected data needs, pattern recognition. |

| **In Memory** | Need for instant/real-time performance and processing, data caching, and temporary storage of data. | 

| **NoSQL** | Quick response times/low latency, instant/real-time analytics, constantly-evolving data, server scalability needs. |

| **Object Oriented** | Integration with object-oriented languages (e.g., Java), hierarchy-based data objects, long-term object storage/persistence. |

| **Relational** | ACID compliance (important for large enterprises and essential services), data integrity, sophisticated joins/transactions of multi-table data, structured data. |

| **Time Oriented** | Time-series analytics, monitoring and metrics, sensor-related and IoT/embedded system data, data sequencing/patterns. |

| **Vector Based** | Quick retrieval, machine learning (including complex tasks), similarity searching. |



  



## 3. Scaling Features

  

Four of the unique ways that databases scale (and can handle increasing traffic/data requests) include...

    

| Feature | Explanation |

| ------ | ------ |

| **Indexing** | This is the use of 'pointers', in which keys in memory (indexes, or indices) are mapped to rows/records in a table. |

| **Caching** | By searching for data in a memory cache, performance of reads can be improved (no need to request data from a database, preserving time and memory). |

| **Sharding** | This splits rows/records and columns/fields across multiple servers/devices, potentially providing load balancing. |

| **Replications** | This also provides load balancing by replicating data (for reading purposes) across multiple servers/devices. Write responsibilities can still be allocated to one database (the 'primary' database) to preserve data integrity. |

  



  

## 4. Supplemental Resources

  

* *[Data Repositories Overview Guide](https://github.com/chaseofthejungle/data-repositories-overview)*

* *[List of Apache Databases](https://projects.apache.org/projects.html?category#database)*

* *[MongoDB Official Website](https://www.mongodb.com/)*