ArangoDB vs Liquibase

Overview

Liquibase

Stacks639

Followers648

Votes70

GitHub Stars5.3K

Forks1.9K

ArangoDB

Stacks273

Followers442

Votes192

ArangoDB vs Liquibase: What are the differences?

<Write Introduction here>

Data Model: ArangoDB is a multi-model database, supporting key-value, document, and graph models, while Liquibase is a database schema management tool focused on managing and applying changes to relational databases.
Use Case: ArangoDB is suitable for applications requiring a flexible data model that can handle complex relationships, while Liquibase is more focused on managing and versioning database schemas for applications with relational databases.
Query Language: ArangoDB uses AQL (ArangoDB Query Language) to interact with the database, while Liquibase uses SQL for database schema management operations.
Deployment: ArangoDB is typically deployed as a standalone database server, while Liquibase is integrated into application development workflows to manage database schema changes alongside code changes.
Consistency: ArangoDB ensures data consistency through its multi-model architecture, combining multiple data models within a single database instance, while Liquibase focuses on maintaining consistency and versioning of database schemas.
Community Support: ArangoDB has a strong community support base due to its diverse range of features and models, while Liquibase has a dedicated user community focused on database schema management and versioning tools.

In Summary, ArangoDB and Liquibase differ in their data models, use cases, query languages, deployment methods, consistency mechanisms, and community support levels.

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CLI (Node.js)

Manual

Detailed Comparison

Liquibase	ArangoDB
Liquibase is th leading open-source tool for database schema change management. Liquibase helps teams track, version, and deploy database schema and logic changes so they can automate their database code process with their app code process.	A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.
Supports code branching and merging;Supports multiple developers;Supports multiple database types;Supports XML, YAML, JSON and SQL formats;Supports context-dependent logic;Cluster-safe database upgrades;Generate Database change documentation;Rollbacks;Generate Database "diff's";Run through your build process, embedded in your application or on demand;Automatically generate SQL scripts for DBA code review;Does not require a live database connection;Stored logic	multi-model nosql db; acid; transactions; javascript; database; nosql; sharding; replication; query language; joins; aql; documents; graphs; key-values; graphdb
Statistics
GitHub Stars 5.3K	GitHub Stars -
GitHub Forks 1.9K	GitHub Forks -
Stacks 639	Stacks 273
Followers 648	Followers 442
Votes 70	Votes 192
Pros & Cons
Pros 18 Many DBs supported 18 Great database tool 12 Easy setup 8 Database independent migration scripts 5 Database version controller Cons 5 Documentation is disorganized 5 No vendor specifics in XML format - needs workarounds	Pros 37 Grahps and documents in one DB 26 Intuitive and rich query language 25 Good documentation 25 Open source 21 Joins for collections Cons 3 Web ui has still room for improvement 2 No support for blueprints standard, using custom AQL
Integrations
Amazon RDS for MariaDB Travis CI SAP HANA Oracle PostgreSQL Sybase jFrog GitHub Actions Firebird IBM DB2	No integrations available

What are some alternatives to Liquibase, ArangoDB?

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

dbForge Studio for MySQL

It is the universal MySQL and MariaDB client for database management, administration and development. With the help of this intelligent MySQL client the work with data and code has become easier and more convenient. This tool provides utilities to compare, synchronize, and backup MySQL databases with scheduling, and gives possibility to analyze and report MySQL tables data.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

dbForge Studio for Oracle

It is a powerful integrated development environment (IDE) which helps Oracle SQL developers to increase PL/SQL coding speed, provides versatile data editing tools for managing in-database and external data.

Related Comparisons

ArangoDB vs Liquibase: What are the differences?

<Write Introduction here>

Data Model: ArangoDB is a multi-model database, supporting key-value, document, and graph models, while Liquibase is a database schema management tool focused on managing and applying changes to relational databases.
Use Case: ArangoDB is suitable for applications requiring a flexible data model that can handle complex relationships, while Liquibase is more focused on managing and versioning database schemas for applications with relational databases.
Query Language: ArangoDB uses AQL (ArangoDB Query Language) to interact with the database, while Liquibase uses SQL for database schema management operations.
Deployment: ArangoDB is typically deployed as a standalone database server, while Liquibase is integrated into application development workflows to manage database schema changes alongside code changes.
Consistency: ArangoDB ensures data consistency through its multi-model architecture, combining multiple data models within a single database instance, while Liquibase focuses on maintaining consistency and versioning of database schemas.
Community Support: ArangoDB has a strong community support base due to its diverse range of features and models, while Liquibase has a dedicated user community focused on database schema management and versioning tools.

In Summary, ArangoDB and Liquibase differ in their data models, use cases, query languages, deployment methods, consistency mechanisms, and community support levels.

ArangoDB vs Liquibase

Overview