DBMS > AnzoGraph DB vs. BigObject vs. ScyllaDB vs. SQLite

System Properties Comparison AnzoGraph DB vs. BigObject vs. ScyllaDB vs. SQLite

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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

Analytic DBMS for real-time computations and queries

Cassandra and DynamoDB compatible wide column store

Widely used embeddable, in-process RDBMS

Primary database model

Graph DBMS
RDF store

Relational DBMS

a hierachical model (tree) can be imposed

Wide column store

Relational DBMS

Secondary database models

Key-value store

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

Score	0.22
Rank	#302	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	0.13
Rank	#329	Overall
	#147	Relational DBMS

Score	4.15
Rank	#69	Overall
	#5	Wide column stores

Score	103.35
Rank	#10	Overall
	#7	Relational DBMS

Website

cambridgesemantics.com/anzograph

bigobject.io

www.scylladb.com

www.sqlite.org

Technical documentation

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

docs.bigobject.io

docs.scylladb.com

www.sqlite.org/docs.html

Developer

Cambridge Semantics

BigObject, Inc.

ScyllaDB

Dwayne Richard Hipp

Initial release

2018

2015

2000

Current release

2.3, January 2021

ScyllaDB Open Source 6.0.2, July 2024

3.46.1 (13 August 2024), August 2024

License

Commercial or Open Source

commercial

free trial version available

commercial

free community edition available

Open Source

Open Source (AGPL), commercial license available

Open Source

Public Domain

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux

server-less

Data scheme

Schema-free and OWL/RDFS-schema support

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

cluster global secondary indices

yes

SQL

Support of SQL

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

SQL-like DML and DDL statements

SQL-like DML and DDL statements (CQL)

yes

SQL-92 is not fully supported

APIs and other access methods

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

fluentd
ODBC
RESTful HTTP API

Proprietary protocol (CQL)

compatible with CQL (Cassandra Query Language, an SQL-like language)
RESTful HTTP API (DynamoDB compatible)
Thrift

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

C++
Java
Python

For CQL interface: C#, C++, Clojure, Erlang, Go, Haskell, Java, JavaScript, Node.js, Perl, PHP, Python, Ruby, Rust, Scala
For DynamoDB interface: .Net, ColdFusion, Erlang, Groovy, Java, JavaScript, Perl, PHP, Python, Ruby

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

user defined functions and aggregates

Lua

yes, Lua

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

none

selectable replication factor

Representation of geographical distribution of servers is possible

none

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

none

Eventual Consistency
Tunable Consistency

can be individually decided for each write operation

Foreign keys

Referential integrity

not needed in graphs

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

ACID

Atomicity and isolation are supported for single operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

via file-system locks

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

in-memory tables

yes

User concepts

Access control

Access rights for users and roles

Access rights for users can be defined per object

More information provided by the system vendor

Specific characteristics

ScyllaDB is engineered to deliver predictable performance at scale. It’s adopted...
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Competitive advantages

Highly-performant (efficiently utilizes full resources of a node and network; millions...
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Typical application scenarios

ScyllaDB is ideal for applications that require high throughput and low latency at...
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Key customers

Discord, Epic Games, Expedia, Zillow, Comcast, Disney+ Hotstar, Samsung, ShareChat,...
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Market metrics

ScyllaDB typically offers ~75% total cost of ownership savings, with ~5X higher throughput...
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Licensing and pricing models

ScyllaDB Open Source - free open source software (AGPL) ScyllaDB Enterprise - subscription-based...
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