MariaDB vs ArangoDB : Which is Better?

MariaDB icon

MariaDB

MariaDB is a community-developed fork of the MySQL relational database management system intended to remain free under the GNU GPL. Developed by MariaDB Foundation

License: Open Source

Categories: Development

Apps available for Windows Linux BSD

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ArangoDB icon

ArangoDB

ArangoDB is a NoSQL multi-modelPro Hibernate and MongoDB database. Developed by triAGENS GmbH

License: Open Source

Categories: Development

Apps available for Mac OS X Windows Linux

MariaDB VS ArangoDB

ArangoDB is a multi-model database that excels in handling JSON data and graph data, making it suitable for complex data relationships. In contrast, MariaDB is a relational database that offers robust SQL support and is best suited for structured data and traditional applications.

MariaDB

Pros:

  • Well-established relational database
  • Robust SQL support and relational data management
  • ACID compliance for transactions
  • High performance with optimized storage engine
  • Wide range of tools and libraries available
  • Strong community and support
  • Good for structured data and traditional applications
  • Advanced indexing capabilities
  • Support for stored procedures and triggers
  • Flexible partitioning options

Cons:

  • Less flexible with non-relational data
  • Scaling can be more challenging than NoSQL solutions
  • Limited support for JSON and document-oriented data
  • Not designed for graph-based queries
  • Can require more maintenance and optimization

ArangoDB

Pros:

  • Flexible multi-model database (Document, Key/Value, Graph)
  • Native support for JSON data
  • Strong graph database capabilities
  • Scalable architecture with horizontal scaling
  • Supports complex queries with AQL (ArangoDB Query Language)
  • Good performance for large datasets
  • Schema-less design for agile development
  • Built-in replication and sharding
  • Rich community and documentation
  • Open-source with a commercial option

Cons:

  • Less suited for strictly relational data
  • Learning curve for AQL
  • Limited support for traditional SQL features
  • Less mature than SQL databases in some areas
  • Complexity in managing multi-model features

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