DBMS > Google Cloud Datastore vs. HBase vs. Microsoft Azure Cosmos DB vs. SAP Adaptive Server vs. Sequoiadb

System Properties Comparison Google Cloud Datastore vs. HBase vs. Microsoft Azure Cosmos DB vs. SAP Adaptive Server vs. Sequoiadb

Editorial information provided by DB-Engines

Name

Description

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

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

Globally distributed, horizontally scalable, multi-model database service

The SAP (Sybase) Adaptive Server Enterprise (ASE) is an enterprise-class RDBMS

NewSQL database with distributed OLTP and SQL

Primary database model

Document store

Wide column store

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Document store
Relational DBMS

Secondary database models

Spatial DBMS

with Boeing's Spatial Query Server

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

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	35.09
Rank	#25	Overall
	#17	Relational DBMS

Score	0.50
Rank	#258	Overall
	#41	Document stores
	#120	Relational DBMS

Website

cloud.google.com/datastore

hbase.apache.org

azure.microsoft.com/services/cosmos-db

www.sap.com/products/technology-platform/sybase-ase.html

www.sequoiadb.com

Technical documentation

cloud.google.com/datastore/docs

hbase.apache.org/book.html

learn.microsoft.com/azure/cosmos-db

help.sap.com/docs/SAP_ASE

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

Google

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Microsoft

SAP, Sybase

Sequoiadb Ltd.

Initial release

2008

2014

1987

2013

Current release

2.3.4, January 2021

16.0

License

Commercial or Open Source

commercial

Open Source

Apache version 2

commercial

Open Source

Server: AGPL; Client: Apache V2

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 and C++

C++

Server operating systems

hosted

Linux
Unix
Windows

using Cygwin

hosted

AIX
HP-UX
Linux
Solaris
Unix
Windows

Linux

Data scheme

schema-free

schema-free, schema definition possible

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes, details here

options to bring your own types, AVRO

yes

JSON types

yes

oid, date, timestamp, binary, regex

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

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like query language (GQL)

SQL-like query language

yes

SQL-like query language

APIs and other access methods

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

Java API
RESTful HTTP API
Thrift

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

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

proprietary protocol using JSON

Supported programming languages

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

C
C#
C++
Groovy
Java
PHP
Python
Scala

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

C
C++
Cobol
Java
Java
Perl
PHP
Python

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

using Google App Engine

yes

Coprocessors in Java

JavaScript

Java and Transact-SQL

JavaScript

Triggers

Callbacks using the Google Apps Engine

yes

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

Multi-source replication
Source-replica replication

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

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 or Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Eventual 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

Single row ACID (across millions of columns)

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Document is locked during a transaction

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

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

simple password-based access control

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