DBMS > Google BigQuery vs. Microsoft Azure Synapse Analytics vs. openGemini vs. Snowflake vs. TimesTen

System Properties Comparison Google BigQuery vs. Microsoft Azure Synapse Analytics vs. openGemini vs. Snowflake vs. TimesTen

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

An open source distributed Time Series DBMS with high concurrency, high performance, and high scalability

Cloud-based data warehousing service for structured and semi-structured data

An in-memory SQL relational database that delivers microsecond response and high throughput for OLTP applications. TimesTen can be deployed as a standalone database or as a cache to a backend Oracle database.

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

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

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	18.98
Rank	#31	Overall
	#19	Relational DBMS

Score	0.00
Rank	#385	Overall
	#40	Time Series DBMS

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Score	1.26
Rank	#164	Overall
	#75	Relational DBMS

Website

cloud.google.com/bigquery

azure.microsoft.com/services/synapse-analytics

www.opengemini.org
github.com/openGemini

www.snowflake.com

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

cloud.google.com/bigquery/docs

docs.microsoft.com/azure/synapse-analytics

docs.opengemini.org/guide

docs.snowflake.net/manuals/index.html

docs.oracle.com/en/database/other-databases/timesten/index.html

Developer

Google

Microsoft

Huawei and openGemini community

Snowflake Computing Inc.

Oracle

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2010

2016

2022

2014

1998

Current release

1.1, July 2023

Release 22.1

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

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

C++

Server operating systems

hosted

Linux
Windows

hosted

IBM AIX Power PC 64-bit
Linux arm64
Linux x86-64
Solaris SPARC 64
Solaris SPARC/x86
Solaris x86-64

Data scheme

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

Typing

predefined data types such as float or date

yes

Integer, Float, Boolean, String

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

HTTP REST

CLI Client
JDBC
ODBC

ODBC
ODP.NET
Oracle Call Interface (OCI)
Pro*C/C++ programming interfaces
SQL and PL/SQL via JDBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
Java
PHP

C
C++
Go
Java
JavaScript (Node.js)
Python
Rust

JavaScript (Node.js)
Python

C
C++
Java
Node.js
PL/SQL
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Transact SQL

user defined functions

PL/SQL

Triggers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

Sharding, horizontal partitioning

Sharding

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

by means of logfiles and checkpoints

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

Administrators and common users accounts

Users with fine-grained authorization concept, user roles and pluggable authentication

fine grained access rights according to SQL-standard

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