DBMS > Coveo vs. EventStoreDB vs. Google BigQuery vs. HEAVY.AI vs. Riak KV

System Properties Comparison Coveo vs. EventStoreDB vs. Google BigQuery vs. HEAVY.AI vs. Riak KV

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

Industrial-strength, open-source database solution built from the ground up for event sourcing.

Large scale data warehouse service with append-only tables

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Distributed, fault tolerant key-value store

Primary database model

with links between data sets and object tags for the creation of secondary indexes

Secondary database models

Spatial DBMS

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

Score	2.11
Rank	#118	Overall
	#11	Search engines

Score	1.19
Rank	#173	Overall
	#1	Event Stores

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Website

www.coveo.com

www.eventstore.com

cloud.google.com/bigquery

github.com/heavyai/heavydb
www.heavy.ai

Technical documentation

docs.coveo.com

developers.eventstore.com

cloud.google.com/bigquery/docs

docs.heavy.ai

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Coveo

Event Store Limited

Google

HEAVY.AI, Inc.

OpenSource, formerly Basho Technologies

Initial release

2012

2010

2016

2009

Current release

21.2, February 2021

5.10, January 2022

3.2.0, December 2022

License

Commercial or Open Source

commercial

Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache version 2, commercial enterprise edition

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

C++ and CUDA

Erlang

Server operating systems

hosted

Linux
Windows

hosted

Linux

Linux
OS X

Data scheme

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

yes

schema-free

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

restricted

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP API

RESTful HTTP/JSON API

JDBC
ODBC
Thrift
Vega

HTTP API
Native Erlang Interface

Supported programming languages

C#
Java
JavaScript
Python

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

All languages supporting JDBC/ODBC/Thrift
Python

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

yes

none

Sharding

Round robin

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

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.

yes

User concepts

Access control

granular access controls, API key management, content filters

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

yes, using Riak Security

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