DBMS > atoti vs. FatDB vs. Google BigQuery vs. ScyllaDB vs. SurrealDB

System Properties Comparison atoti vs. FatDB vs. Google BigQuery vs. ScyllaDB vs. SurrealDB

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

Large scale data warehouse service with append-only tables

Cassandra and DynamoDB compatible wide column store

A fully ACID transactional, developer-friendly, multi-model DBMS

Primary database model

Object oriented DBMS

Document store
Key-value store

Relational DBMS

Wide column store

Document store
Graph DBMS

Secondary database models

Key-value store

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

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	4.75
Rank	#68	Overall
	#5	Wide column stores

Score	0.86
Rank	#203	Overall
	#34	Document stores
	#18	Graph DBMS

Website

atoti.io

cloud.google.com/bigquery

www.scylladb.com

surrealdb.com

Technical documentation

docs.atoti.io

cloud.google.com/bigquery/docs

docs.scylladb.com

surrealdb.com/docs

Developer

ActiveViam

FatCloud

Google

ScyllaDB

SurrealDB Ltd

Initial release

2012

2010

2015

2022

Current release

ScyllaDB Open Source 5.4.1, January 2024

v1.5.0, May 2024

License

Commercial or Open Source

commercial

free versions available

commercial

Open Source

Open Source (AGPL), commercial license available

Open Source

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.

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Implementation language

Java

C++

Rust

Server operating systems

Windows

hosted

Linux

Linux
macOS
Windows

Data scheme

schema-free

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

cluster global secondary indices

SQL

Support of SQL

Multidimensional Expressions (MDX)

Via inetgration in SQL Server

yes

SQL-like DML and DDL statements (CQL)

SQL-like query language

APIs and other access methods

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

RESTful HTTP/JSON API

Proprietary protocol (CQL)

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

GraphQL
RESTful HTTP API
WebSocket

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Deno
Go
JavaScript (Node.js)
Rust

Server-side scripts

Stored procedures

Python

yes

via applications

user defined functions

in JavaScript

yes, Lua

Triggers

yes

via applications

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Representation of geographical distribution of servers is possible

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual Consistency
Tunable Consistency

can be individually decided for each write operation

Foreign keys

Referential integrity

Transaction concepts

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

Since BigQuery is designed for querying data

Atomicity and isolation are supported for single operations

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (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

in-memory tables

User concepts

Access control

Can implement custom security layer via applications

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

Google Cloud Identity & Access Management (IAM)

Access rights for users can be defined per object

yes, based on authentication and database rules

More information provided by the system vendor

Specific characteristics

ScyllaDB is engineered to deliver predictable performance at scale. It’s adopted...
» more

Competitive advantages

Highly-performant (efficiently utilizes full resources of a node and network; millions...
» more

Typical application scenarios

ScyllaDB is ideal for applications that require high throughput and low latency at...
» more

Key customers

Discord, Epic Games, Expedia, Zillow, Comcast, Disney+ Hotstar, Samsung, ShareChat,...
» more

Market metrics

ScyllaDB typically offers ~75% total cost of ownership savings, with ~5X higher throughput...
» more

Licensing and pricing models

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

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