DBMS > Fauna vs. Google BigQuery vs. ScyllaDB vs. SiteWhere

System Properties Comparison Fauna vs. Google BigQuery vs. ScyllaDB vs. SiteWhere

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Editorial information provided by DB-Engines

Name

Description

Fauna provides a web-native interface, with support for GraphQL and custom business logic that integrates seamlessly with the rest of the serverless ecosystem. The underlying globally distributed storage and compute platform is fast, consistent, and reliable, with a modern security infrastructure.

Large scale data warehouse service with append-only tables

Cassandra and DynamoDB compatible wide column store

M2M integration platform for persisting/querying time series data

Primary database model

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Relational DBMS

Wide column store

Time Series DBMS

Secondary database models

Key-value store

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

Score	1.52
Rank	#153	Overall
	#26	Document stores
	#14	Graph DBMS
	#71	Relational DBMS
	#13	Time Series DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	4.75
Rank	#68	Overall
	#5	Wide column stores

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Website

fauna.com

cloud.google.com/bigquery

www.scylladb.com

github.com/sitewhere/sitewhere

Technical documentation

docs.fauna.com

cloud.google.com/bigquery/docs

docs.scylladb.com

sitewhere1.sitewhere.io/index.html

Developer

Fauna, Inc.

Google

ScyllaDB

SiteWhere

Initial release

2014

2010

2015

2010

Current release

ScyllaDB Open Source 5.4.1, January 2024

License

Commercial or Open Source

commercial

Open Source

Open Source (AGPL), commercial license available

Open Source

Common Public Attribution License Version 1.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Scala

C++

Java

Server operating systems

hosted

Linux

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

predefined scheme

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

yes

SQL-like DML and DDL statements (CQL)

APIs and other access methods

RESTful HTTP API

RESTful HTTP/JSON API

Proprietary protocol (CQL)

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

HTTP REST

Supported programming languages

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

.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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes, Lua

Triggers

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

consistent hashing

none

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication factor

Representation of geographical distribution of servers is possible

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Tunable Consistency

can be individually decided for each write operation

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

Atomicity and isolation are supported for single operations

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

in-memory tables

User concepts

Access control

Identity management, authentication, and access control

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

Users with fine-grained authorization concept

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...
» more

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