DBMS > AnzoGraph DB vs. Apache Druid vs. Google BigQuery

System Properties Comparison AnzoGraph DB vs. Apache Druid vs. Google BigQuery

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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

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Large scale data warehouse service with append-only tables

Primary database model

Graph DBMS
RDF store

Relational DBMS
Time Series DBMS

Relational DBMS

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	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Website

cambridgesemantics.com/anzograph

druid.apache.org

cloud.google.com/bigquery

Technical documentation

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

druid.apache.org/docs/latest/design

cloud.google.com/bigquery/docs

Developer

Cambridge Semantics

Apache Software Foundation and contributors

Google

Initial release

2018

2012

2010

Current release

2.3, January 2021

29.0.1, April 2024

License

Commercial or Open Source

commercial

free trial version available

Open Source

Apache license v2

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

Java

Server operating systems

Linux

Linux
OS X
Unix

hosted

Data scheme

Schema-free and OWL/RDFS-schema support

yes

schema-less columns are supported

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

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

SQL for querying

yes

APIs and other access methods

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

JDBC
RESTful HTTP/JSON API

RESTful HTTP/JSON API

Supported programming languages

C++
Java
Python

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

in JavaScript

Triggers

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

manual/auto, time-based

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

yes, via HDFS, S3 or other storage engines

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

Transaction concepts

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

ACID

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

Access rights for users and roles

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

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

Google Cloud Identity & Access Management (IAM)

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