DBMS > Google BigQuery vs. Microsoft Azure Synapse Analytics vs. Sequoiadb vs. Snowflake vs. YottaDB

System Properties Comparison Google BigQuery vs. Microsoft Azure Synapse Analytics vs. Sequoiadb vs. Snowflake vs. YottaDB

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

NewSQL database with distributed OLTP and SQL

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

A fast and solid embedded Key-value store

Primary database model

Relational DBMS

Document store
Relational DBMS

Relational DBMS

Key-value store

Secondary database models

Relational DBMS

using the Octo plugin

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	0.42
Rank	#263	Overall
	#41	Document stores
	#121	Relational DBMS

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Score	0.21
Rank	#309	Overall
	#43	Key-value stores

Website

cloud.google.com/bigquery

azure.microsoft.com/services/synapse-analytics

www.sequoiadb.com

www.snowflake.com

yottadb.com

Technical documentation

cloud.google.com/bigquery/docs

docs.microsoft.com/azure/synapse-analytics

www.sequoiadb.com/en/index.php?m=Files&a=index

docs.snowflake.net/manuals/index.html

yottadb.com/resources/documentation

Developer

Google

Microsoft

Sequoiadb Ltd.

Snowflake Computing Inc.

YottaDB, LLC

Initial release

2010

2016

2013

2014

2001

License

Commercial or Open Source

commercial

Open Source

Server: AGPL; Client: Apache V2

commercial

Open Source

AGPL 3.0

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++

Server operating systems

hosted

Linux

hosted

Docker
Linux

Data scheme

yes

schema-free

yes

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

schema-free

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

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

by using the Octo plugin

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

proprietary protocol using JSON

CLI Client
JDBC
ODBC

PostgreSQL wire protocol

using the Octo plugin
Proprietary protocol

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
Java
PHP

.Net
C++
Java
PHP
Python

JavaScript (Node.js)
Python

C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Transact SQL

JavaScript

user defined functions

Triggers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

Sharding, horizontal partitioning

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

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

Document is locked during a transaction

ACID

optimistic locking

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.

yes

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

yes

simple password-based access control

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

Users and groups based on OS-security mechanisms

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