DBMS > Google BigQuery vs. Graph Engine vs. Microsoft Azure Synapse Analytics vs. TerminusDB

System Properties Comparison Google BigQuery vs. Graph Engine vs. Microsoft Azure Synapse Analytics vs. TerminusDB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Relational DBMS

Graph DBMS
Key-value store

Relational DBMS

Graph DBMS

Secondary database models

Document store
RDF store

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

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	0.56
Rank	#241	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	18.98
Rank	#31	Overall
	#19	Relational DBMS

Score	0.19
Rank	#311	Overall
	#27	Graph DBMS

Website

cloud.google.com/bigquery

www.graphengine.io

azure.microsoft.com/services/synapse-analytics

terminusdb.com

Technical documentation

cloud.google.com/bigquery/docs

www.graphengine.io/docs/manual

docs.microsoft.com/azure/synapse-analytics

terminusdb.github.io/terminusdb/#

Developer

Google

Microsoft

DataChemist Ltd.

Initial release

2010

2016

2018

Current release

11.0.0, January 2023

License

Commercial or Open Source

commercial

Open Source

MIT License

commercial

Open Source

GPL V3

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

.NET and C

C++

Prolog, Rust

Server operating systems

hosted

.NET

hosted

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

SQL-like query language (WOQL)

APIs and other access methods

RESTful HTTP/JSON API

RESTful HTTP API

ADO.NET
JDBC
ODBC

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
F#
Visual Basic

C#
Java
PHP

JavaScript
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Transact SQL

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Sharding, horizontal partitioning

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Journaling Streams

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

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

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

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

yes

in-memory journaling

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

yes

Role-based access control

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