DBMS > Google Cloud Bigtable vs. H2GIS vs. HyperSQL vs. Microsoft Azure Synapse Analytics

System Properties Comparison Google Cloud Bigtable vs. H2GIS vs. HyperSQL vs. Microsoft Azure Synapse Analytics

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Editorial information provided by DB-Engines

Name

Description

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Spatial extension of H2

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

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

Primary database model

Key-value store
Wide column store

Spatial DBMS

Relational DBMS

Secondary database models

Relational DBMS

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

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Score	3.49
Rank	#87	Overall
	#47	Relational DBMS

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Website

cloud.google.com/bigtable

www.h2gis.org

hsqldb.org

azure.microsoft.com/services/synapse-analytics

Technical documentation

cloud.google.com/bigtable/docs

www.h2gis.org/docs/home

hsqldb.org/web/hsqlDocsFrame.html

docs.microsoft.com/azure/synapse-analytics

Developer

Google

CNRS

Microsoft

Initial release

2015

2013

2001

2016

Current release

2.7.2, June 2023

License

Commercial or Open Source

commercial

Open Source

LGPL 3.0

Open Source

based on BSD license

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

Java

C++

Server operating systems

hosted

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

hosted

Data scheme

schema-free

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

APIs and other access methods

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

HTTP API

JDBC via HTTP
JDBC
ODBC

ADO.NET
JDBC
ODBC

Supported programming languages

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

Java

All languages supporting JDBC/ODBC
Java

C#
Java
PHP

Server-side scripts

Stored procedures

yes

based on H2

Java, SQL

Transact SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Internal replication in Colossus, and regional replication between two clusters in different zones

yes

based on H2

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

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

Atomic single-row operations

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 rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

yes

based on H2

fine grained access rights according to SQL-standard

yes

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More resources

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