DBMS > AnzoGraph DB vs. Apache Drill vs. Google BigQuery vs. Sequoiadb

System Properties Comparison AnzoGraph DB vs. Apache Drill vs. Google BigQuery vs. Sequoiadb

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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

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

Large scale data warehouse service with append-only tables

NewSQL database with distributed OLTP and SQL

Primary database model

Graph DBMS
RDF store

Document store
Relational DBMS

Relational DBMS

Document store
Relational DBMS

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

Score	0.29
Rank	#303	Overall
	#25	Graph DBMS
	#14	RDF stores

Score	2.02
Rank	#124	Overall
	#22	Document stores
	#59	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.50
Rank	#258	Overall
	#41	Document stores
	#120	Relational DBMS

Website

cambridgesemantics.com/anzograph

drill.apache.org

cloud.google.com/bigquery

www.sequoiadb.com

Technical documentation

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

drill.apache.org/docs

cloud.google.com/bigquery/docs

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

Cambridge Semantics

Apache Software Foundation

Google

Sequoiadb Ltd.

Initial release

2018

2012

2010

2013

Current release

2.3, January 2021

1.20.3, January 2023

License

Commercial or Open Source

commercial

free trial version available

Open Source

Apache Version 2

commercial

Open Source

Server: AGPL; Client: Apache V2

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

Linux
OS X
Windows

hosted

Linux

Data scheme

Schema-free and OWL/RDFS-schema support

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

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 SELECT statement is SQL:2003 compliant

yes

SQL-like query language

APIs and other access methods

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

JDBC
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

proprietary protocol using JSON

Supported programming languages

C++
Java
Python

C++

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

in JavaScript

JavaScript

Triggers

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

none

Immediate Consistency

Eventual 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

Document is locked during a transaction

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Depending on the underlying data source

yes

In-memory capabilities

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

yes

Depending on the underlying data source

User concepts

Access control

Access rights for users and roles

Depending on the underlying data source

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

Google Cloud Identity & Access Management (IAM)

simple password-based access control

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