DBMS > Google BigQuery vs. Microsoft Azure Synapse Analytics vs. TinkerGraph

System Properties Comparison Google BigQuery vs. Microsoft Azure Synapse Analytics vs. TinkerGraph

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

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

A lightweight, in-memory graph engine that serves as a reference implementation of the TinkerPop3 API

Primary database model

Relational DBMS

Graph DBMS

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

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	22.71
Rank	#30	Overall
	#18	Relational DBMS

Score	0.12
Rank	#344	Overall
	#34	Graph DBMS

Website

cloud.google.com/bigquery

azure.microsoft.com/services/synapse-analytics

tinkerpop.apache.org/docs/current/reference/#tinkergraph-gremlin

Technical documentation

cloud.google.com/bigquery/docs

docs.microsoft.com/azure/synapse-analytics

Developer

Google

Microsoft

Initial release

2010

2016

2009

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

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++

Java

Server operating systems

hosted

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.

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

TinkerPop 3

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
Java
PHP

Groovy
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Transact SQL

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding, horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Foreign keys

Referential integrity

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

yes

Relationships in graphs

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

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

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.