DBMS > Google BigQuery vs. H2 vs. Microsoft Azure Synapse Analytics vs. Warp 10 vs. Yanza

System Properties Comparison Google BigQuery vs. H2 vs. Microsoft Azure Synapse Analytics vs. Warp 10 vs. Yanza

Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Large scale data warehouse service with append-only tables

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

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

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Time Series DBMS for IoT Applications

Primary database model

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	8.33
Rank	#46	Overall
	#30	Relational DBMS

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Score	0.14
Rank	#344	Overall
	#32	Time Series DBMS

Website

cloud.google.com/bigquery

www.h2database.com

azure.microsoft.com/services/synapse-analytics

www.warp10.io

yanza.com

Technical documentation

cloud.google.com/bigquery/docs

www.h2database.com/html/main.html

docs.microsoft.com/azure/synapse-analytics

www.warp10.io/content/02_Getting_started

Developer

Google

Thomas Mueller

Microsoft

SenX

Yanza

Initial release

2010

2005

2016

2015

Current release

2.2.220, July 2023

License

Commercial or Open Source

commercial

Open Source

dual-licence (Mozilla public license, Eclipse public license)

commercial

Open Source

Apache License 2.0

commercial

free version available

Cloud-based only

Only available as a cloud service

yes

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

Java

Server operating systems

hosted

All OS with a Java VM

hosted

Linux
OS X
Windows

Windows

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

JDBC
ODBC

ADO.NET
JDBC
ODBC

HTTP API
Jupyter
WebSocket

HTTP API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java

C#
Java
PHP

any language that supports HTTP calls

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Java Stored Procedures and User-Defined Functions

Transact SQL

yes

WarpScript

Triggers

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

none

Sharding, horizontal partitioning

Sharding

based on HBase

none

Replication methods

Methods for redundantly storing data on multiple nodes

With clustering: 2 database servers on different computers operate on identical copies of a database

yes

selectable replication factor

based on HBase

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

based on HBase

Immediate Consistency

Foreign keys

Referential integrity

yes

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

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

yes, multi-version concurrency control (MVCC)

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)

fine grained access rights according to SQL-standard

yes

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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