DBMS > Alibaba Cloud Table Store vs. Derby vs. Google Cloud Bigtable vs. Microsoft SQL Server vs. Transwarp Hippo

System Properties Comparison Alibaba Cloud Table Store vs. Derby vs. Google Cloud Bigtable vs. Microsoft SQL Server vs. Transwarp Hippo

Editorial information provided by DB-Engines

Name

Description

A fully managed Wide Column Store for large quantities of semi-structured data with real-time access

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

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

Microsofts flagship relational DBMS

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

Primary database model

Wide column store

Relational DBMS

Key-value store
Wide column store

Relational DBMS

Vector DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.26
Rank	#301	Overall
	#12	Wide column stores

Score	4.71
Rank	#69	Overall
	#37	Relational DBMS

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

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.00
Rank	#383	Overall
	#13	Vector DBMS

Website

www.alibabacloud.com/product/table-store

db.apache.org/derby

cloud.google.com/bigtable

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

www.transwarp.cn/en/subproduct/hippo

Technical documentation

www.alibabacloud.com/help/en/tablestore

db.apache.org/derby/manuals/index.html

cloud.google.com/bigtable/docs

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

Developer

Alibaba

Apache Software Foundation

Google

Microsoft

Initial release

2016

1997

2015

1989

2023

Current release

10.17.1.0, November 2023

SQL Server 2022, November 2022

1.0, May 2023

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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Database as a Service

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Implementation language

Java

C++

Server operating systems

hosted

All OS with a Java VM

hosted

Linux
Windows

Linux
macOS

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

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

APIs and other access methods

HTTP API

JDBC

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

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

RESTful HTTP API

Supported programming languages

Java
Python

Java

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

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

C++
Java
Python

Server-side scripts

Stored procedures

Java Stored Procedures

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

Implicit feature of the cloud service

none

Sharding

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Source-replica replication

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

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

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

Atomic single-row operations

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 based on subaccounts and tokens

fine grained access rights according to SQL-standard

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

fine grained access rights according to SQL-standard

Role based access control and fine grained access rights

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