DBMS > AnzoGraph DB vs. Coveo vs. Google BigQuery vs. Ingres vs. RocksDB

System Properties Comparison AnzoGraph DB vs. Coveo vs. Google BigQuery vs. Ingres vs. RocksDB

Editorial information provided by DB-Engines

Name

Description

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

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

Large scale data warehouse service with append-only tables

Well established RDBMS

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

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

Score	0.23
Rank	#307	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	2.28
Rank	#114	Overall
	#11	Search engines

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	4.11
Rank	#81	Overall
	#44	Relational DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Website

cambridgesemantics.com/anzograph

www.coveo.com

cloud.google.com/bigquery

www.actian.com/databases/ingres

rocksdb.org

Technical documentation

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

docs.coveo.com

cloud.google.com/bigquery/docs

docs.actian.com/ingres

github.com/facebook/rocksdb/wiki

Developer

Cambridge Semantics

Coveo

Google

Actian Corporation

Facebook, Inc.

Initial release

2018

2012

2010

1974

originally developed at University Berkely in early 1970s

2013

Current release

2.3, January 2021

11.2, May 2022

8.11.4, April 2024

License

Commercial or Open Source

commercial

free trial version available

commercial

Open Source

BSD

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++

Server operating systems

Linux

hosted

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

Linux

Data scheme

Schema-free and OWL/RDFS-schema support

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.

but tools for importing/exporting data from/to XML-files available

Secondary indexes

yes

SQL

Support of SQL

SPARQL and SPARQL* as primary query language. Cypher preview.

yes

APIs and other access methods

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

RESTful HTTP API

RESTful HTTP/JSON API

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

C++ API
Java API

Supported programming languages

C++
Java
Python

C#
Java
JavaScript
Python

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

yes

none

horizontal partitioning

Ingres Star to access multiple databases simultaneously

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

yes

Ingres Replicator

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

Transaction concepts

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

ACID

yes

Since BigQuery is designed for querying data

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

MVCC

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

Access rights for users and roles

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

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