DBMS > BoltDB vs. Coveo vs. Databend vs. Google BigQuery

System Properties Comparison BoltDB vs. Coveo vs. Databend vs. Google BigQuery

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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

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

Large scale data warehouse service with append-only tables

Primary database model

Key-value store

Search engine

Relational DBMS

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

Score	0.80
Rank	#215	Overall
	#31	Key-value stores

Score	2.11
Rank	#118	Overall
	#11	Search engines

Score	0.34
Rank	#283	Overall
	#130	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Website

github.com/boltdb/bolt

www.coveo.com

github.com/datafuselabs/databend
www.databend.com

cloud.google.com/bigquery

Technical documentation

docs.coveo.com

docs.databend.com

cloud.google.com/bigquery/docs

Developer

Coveo

Databend Labs

Google

Initial release

2013

2012

2021

2010

Current release

1.0.59, April 2023

License

Commercial or Open Source

Open Source

MIT License

commercial

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Rust

Server operating systems

BSD
Linux
OS X
Solaris
Windows

hosted

hosted
Linux
macOS

hosted

Data scheme

schema-free

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

yes

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

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP API

CLI Client
JDBC
RESTful HTTP API

RESTful HTTP/JSON API

Supported programming languages

C#
Java
JavaScript
Python

Go
Java
JavaScript (Node.js)
Python
Rust

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

Since BigQuery is designed for querying data

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.

User concepts

Access control

granular access controls, API key management, content filters

Users with fine-grained authorization concept, user roles

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

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