DBMS > Google Cloud Bigtable vs. HBase vs. Microsoft SQL Server vs. SpaceTime vs. Yaacomo

System Properties Comparison Google Cloud Bigtable vs. HBase vs. Microsoft SQL Server vs. SpaceTime vs. Yaacomo

Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

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.

Wide-column store based on Apache Hadoop and on concepts of BigTable

Microsofts flagship relational DBMS

SpaceTime is a spatio-temporal DBMS with a focus on performance.

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Key-value store
Wide column store

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Website

cloud.google.com/bigtable

hbase.apache.org

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

www.mireo.com/spacetime

yaacomo.com

Technical documentation

cloud.google.com/bigtable/docs

hbase.apache.org/book.html

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

Developer

Google

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Microsoft

Mireo

Q2WEB GmbH

Initial release

2015

2008

1989

2020

2009

Current release

2.3.4, January 2021

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

Open Source

Apache version 2

commercial

restricted free version is available

commercial

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C++

Server operating systems

hosted

Linux
Unix
Windows

using Cygwin

Linux
Windows

Linux

Android
Linux
Windows

Data scheme

schema-free

schema-free, schema definition possible

yes

Typing

predefined data types such as float or date

options to bring your own types, AVRO

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

A subset of ANSI SQL is implemented

yes

APIs and other access methods

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

Java API
RESTful HTTP API
Thrift

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

RESTful HTTP API

JDBC
ODBC

Supported programming languages

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

C
C#
C++
Groovy
Java
PHP
Python
Scala

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

C#
C++
Python

Server-side scripts

Stored procedures

yes

Coprocessors in Java

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

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

Fixed-grid hypercubes

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

Multi-source replication
Source-replica replication

yes, but depending on the SQL-Server Edition

Real-time block device replication (DRBD)

Source-replica replication

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 or 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

Single row ACID (across millions of columns)

ACID

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

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

fine grained access rights according to SQL-standard

yes

fine grained access rights according to SQL-standard

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