DBMS > BigchainDB vs. Google Cloud Bigtable vs. Hive vs. Kingbase vs. Microsoft SQL Server

System Properties Comparison BigchainDB vs. Google Cloud Bigtable vs. Hive vs. Kingbase vs. Microsoft SQL Server

Editorial information provided by DB-Engines

Name

Description

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

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

data warehouse software for querying and managing large distributed datasets, built on Hadoop

An enterprise-class RDBMS compatible with PostgreSQL and Oracle and widely used in China.

Microsofts flagship relational DBMS

Primary database model

Document store

Key-value store
Wide column store

Relational DBMS

Secondary database models

Document store
Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	0.85
Rank	#208	Overall
	#35	Document stores

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	0.50
Rank	#257	Overall
	#119	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Website

www.bigchaindb.com

cloud.google.com/bigtable

hive.apache.org

www.kingbase.com.cn

www.microsoft.com/en-us/sql-server

Technical documentation

bigchaindb.readthedocs.io/en/latest

cloud.google.com/bigtable/docs

cwiki.apache.org/confluence/display/Hive/Home

learn.microsoft.com/en-US/sql/sql-server

Developer

Google

Apache Software Foundation

initially developed by Facebook

BeiJing KINGBASE Information technologies inc.

Microsoft

Initial release

2016

2015

2012

1999

1989

Current release

3.1.3, April 2022

V8.0, August 2021

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

AGPL v3

commercial

Open Source

Apache Version 2

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Python

Java

C and Java

C++

Server operating systems

Linux

hosted

All OS with a Java VM

Linux
Windows

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

Standard with numerous extensions

yes

APIs and other access methods

CLI Client
RESTful HTTP API

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

JDBC
ODBC
Thrift

ADO.NET
gokb
JDBC
kdbndp
ODBC
PDI
PDO
Pro*C
psycopg2
QT

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

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

C++
Java
PHP
Python

.Net
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by range, list and hash) and vertical partitioning

tables can be distributed across several files (horizontal partitioning); sharding through federation

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

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

selectable replication factor

yes

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

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

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)

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

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