DBMS > BigchainDB vs. Google Cloud Datastore vs. H2 vs. Microsoft Azure SQL Database vs. Snowflake

System Properties Comparison BigchainDB vs. Google Cloud Datastore vs. H2 vs. Microsoft Azure SQL Database vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

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

Database as a Service offering with high compatibility to Microsoft SQL Server

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

Primary database model

Document store

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	0.79
Rank	#212	Overall
	#36	Document stores

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	8.13
Rank	#49	Overall
	#31	Relational DBMS

Score	77.99
Rank	#16	Overall
	#11	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

www.bigchaindb.com

cloud.google.com/datastore

www.h2database.com

azure.microsoft.com/en-us/products/azure-sql/database

www.snowflake.com

Technical documentation

bigchaindb.readthedocs.io/en/latest

cloud.google.com/datastore/docs

www.h2database.com/html/main.html

docs.microsoft.com/en-us/azure/azure-sql

docs.snowflake.net/manuals/index.html

Developer

Google

Thomas Mueller

Microsoft

Snowflake Computing Inc.

Initial release

2016

2008

2005

2010

2014

Current release

2.2.220, July 2023

V12

License

Commercial or Open Source

Open Source

AGPL v3

commercial

Open Source

dual-licence (Mozilla public license, Eclipse public 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

Python

Java

C++

Server operating systems

Linux

hosted

All OS with a Java VM

hosted

Data scheme

schema-free

yes

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

Typing

predefined data types such as float or date

yes, details here

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

SQL-like query language (GQL)

yes

APIs and other access methods

CLI Client
RESTful HTTP API

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

JDBC
ODBC

ADO.NET
JDBC
ODBC

CLI Client
JDBC
ODBC

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java

.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

using Google App Engine

Java Stored Procedures and User-Defined Functions

Transact SQL

user defined functions

Triggers

Callbacks using the Google Apps Engine

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication using Paxos

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

yes, with always 3 replicas available

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes,with MongoDB ord RethinkDB

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

yes

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

fine grained access rights according to SQL-standard

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

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