DBMS > Coveo vs. JaguarDB vs. Realm vs. Vertica

System Properties Comparison Coveo vs. JaguarDB vs. Realm vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

AI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code development

Performant, highly scalable DBMS for AI and IoT applications

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Search engine

Key-value store
Vector DBMS

Document store

Relational DBMS

Column oriented

Secondary database models

Spatial DBMS
Time Series DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	2.14
Rank	#116	Overall
	#11	Search engines

Score	0.00
Rank	#385	Overall
	#61	Key-value stores
	#16	Vector DBMS

Score	7.18
Rank	#52	Overall
	#8	Document stores

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Website

Technical documentation

docs.coveo.com

www.jaguardb.com/support.html

realm.io/docs

vertica.com/documentation

Developer

Coveo

DataJaguar, Inc.

Realm, acquired by MongoDB in May 2019

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2012

2015

2014

2005

Current release

3.3 July 2023

12.0.3, January 2023

License

Commercial or Open Source

commercial

Open Source

GPL V3.0

Open Source

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

the server part. Clients available in other languages

C++

Server operating systems

hosted

Linux

Android
Backend: server-less
iOS
Windows

Linux

Data scheme

hybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configuration

yes

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

RESTful HTTP API

JDBC
ODBC

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

C#
Java
JavaScript
Python

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

.Net
Java

with Android only
Objective-C
React Native
Swift

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

runs within the applications so server-side scripts are unnecessary

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

Change Listeners

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

none

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

none

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

In-Memory realm

User concepts

Access control

granular access controls, API key management, content filters

rights management via user accounts

yes

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

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More resources

DB-Engines blog posts

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