DBMS > Google Cloud Datastore vs. Microsoft Azure Table Storage vs. OpenEdge vs. SAP HANA vs. UniData,UniVerse

System Properties Comparison Google Cloud Datastore vs. Microsoft Azure Table Storage vs. OpenEdge vs. SAP HANA vs. UniData,UniVerse

Editorial information provided by DB-Engines

Name

Description

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

A Wide Column Store for rapid development using massive semi-structured datasets

Application development environment with integrated database management system

In-memory, column based data store. Available as appliance or cloud service

MultiValue database and application server with SQL mapping layer and meta database capabilities

Primary database model

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	6.45
Rank	#67	Overall
	#12	Document stores

Score	5.53
Rank	#77	Overall
	#5	Wide column stores

Score	4.50
Rank	#84	Overall
	#44	Relational DBMS

Score	54.96
Rank	#21	Overall
	#13	Relational DBMS

Score	4.44
Rank	#85	Overall
	#2	Multivalue DBMS

Website

cloud.google.com/datastore

azure.microsoft.com/en-us/services/storage/tables

www.progress.com/openedge

www.sap.com/products/hana.html

www.rocketsoftware.com/products/rocket-u2

Technical documentation

cloud.google.com/datastore/docs

documentation.progress.com/output/ua/OpenEdge_latest

help.sap.com/hana

www.rocketsoftware.com/products/rocket-u2/documentation

Developer

Google

Microsoft

Progress Software Corporation

SAP

Rocket Software

Initial release

2008

2012

1984

2010

1985

Current release

OpenEdge 12.2, March 2020

2.0 SPS05 (June 26, 2020), June 2020

License

Commercial or Open Source

commercial

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Server operating systems

hosted

AIX
HP-UX
Linux
Solaris
Windows

Appliance or cloud-service

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes, details here

yes

optional

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

close to SQL 92

yes

APIs and other access methods

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

RESTful HTTP API

JDBC
ODBC

Java API

JPA
JDBC
ODBC
OLE DB
Proprietary protocol
RESTful HTTP API
SOAP-based API

Supported programming languages

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

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

Progress proprietary ABL (Advanced Business Language)

.Net
Basic

U2 Basic
C
Java

Server-side scripts

Stored procedures

using Google App Engine

yes

SQLScript, R

yes

Triggers

Callbacks using the Google Apps Engine

yes

U2 Basic

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

horizontal partitioning

since Version 11.4

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

Source-replica replication

yes

Source-replica replication

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

optimistic locking

ACID

configurable

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 rights based on private key authentication or shared access signatures

Users and groups

yes

Access rights according to SQL-standard and operating system based

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