DBMS > Databend vs. Google BigQuery vs. mSQL vs. TigerGraph

System Properties Comparison Databend vs. Google BigQuery vs. mSQL vs. TigerGraph

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Editorial information provided by DB-Engines

Name

Description

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

Large scale data warehouse service with append-only tables

mSQL (Mini SQL) is a simple and lightweight RDBMS

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Primary database model

Relational DBMS

Graph DBMS

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

Score	0.34
Rank	#283	Overall
	#130	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1.27
Rank	#169	Overall
	#76	Relational DBMS

Score	1.80
Rank	#138	Overall
	#13	Graph DBMS

Website

github.com/datafuselabs/databend
www.databend.com

cloud.google.com/bigquery

hughestech.com.au/products/msql

www.tigergraph.com

Technical documentation

docs.databend.com

cloud.google.com/bigquery/docs

docs.tigergraph.com

Developer

Databend Labs

Google

Hughes Technologies

Initial release

2021

2010

1994

2017

Current release

1.0.59, April 2023

4.4, October 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free licenses can be provided

commercial

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

Rust

C++

Server operating systems

hosted
Linux
macOS

hosted

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux

Data scheme

yes

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

SQL

Support of SQL

yes

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

SQL-like query language (GSQL)

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

RESTful HTTP/JSON API

JDBC
ODBC

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C++
Delphi
Java
Perl
PHP
Tcl

C++
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

yes

Since BigQuery is designed for querying data

ACID

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.

User concepts

Access control

Users with fine-grained authorization concept, user roles

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

Google Cloud Identity & Access Management (IAM)

Role-based access control

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