DBMS > Google BigQuery vs. Ingres vs. Sadas Engine vs. SurrealDB

System Properties Comparison Google BigQuery vs. Ingres vs. Sadas Engine vs. SurrealDB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Well established RDBMS

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

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

Primary database model

Relational DBMS

Document store
Graph DBMS

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

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	3.63
Rank	#79	Overall
	#43	Relational DBMS

Score	0.00
Rank	#385	Overall
	#159	Relational DBMS

Score	1.11
Rank	#174	Overall
	#30	Document stores
	#17	Graph DBMS

Website

cloud.google.com/bigquery

www.actian.com/databases/ingres

www.sadasengine.com

surrealdb.com

Technical documentation

cloud.google.com/bigquery/docs

docs.actian.com/ingres

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

surrealdb.com/docs

Developer

Google

Actian Corporation

SADAS s.r.l.

SurrealDB Ltd

Initial release

2010

1974

originally developed at University Berkely in early 1970s

2006

2022

Current release

12.0, July 2024

8.0

v1.5.0, May 2024

License

Commercial or Open Source

commercial

free trial version 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.

Implementation language

C++

Rust

Server operating systems

hosted

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

AIX
Linux
Windows

Linux
macOS
Windows

Data scheme

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.

but tools for importing/exporting data from/to XML-files available

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

RESTful HTTP/JSON API

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

JDBC
ODBC
Proprietary protocol

GraphQL
RESTful HTTP API
WebSocket

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C#
C++
Groovy
Java
PHP
Python

Deno
Go
JavaScript (Node.js)
Rust

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Ingres Star to access multiple databases simultaneously

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Ingres Replicator

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

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

managed by 'Learn by Usage'

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

yes, based on authentication and database rules

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