DBMS > Adabas vs. GBase vs. Google BigQuery vs. Google Cloud Datastore vs. Hawkular Metrics

System Properties Comparison Adabas vs. GBase vs. Google BigQuery vs. Google Cloud Datastore vs. Hawkular Metrics

Editorial information provided by DB-Engines

Name

Description

OLTP - DBMS for mainframes and Linux/Unix/Windows environments

used typically together with the Natural programming platform

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

Large scale data warehouse service with append-only tables

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

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

Primary database model

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

Score	2.79
Rank	#102	Overall
	#2	Multivalue DBMS

Score	1.05
Rank	#186	Overall
	#86	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

Website

www.softwareag.com/en_corporate/platform/adabas-natural.html

www.gbase.cn

cloud.google.com/bigquery

cloud.google.com/datastore

www.hawkular.org

Technical documentation

cloud.google.com/bigquery/docs

cloud.google.com/datastore/docs

www.hawkular.org/hawkular-metrics/docs/user-guide

Developer

Software AG

General Data Technology Co., Ltd.

Google

Community supported by Red Hat

Initial release

1971

2004

2010

2008

2014

Current release

GBase 8a, GBase 8s, GBase 8c

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

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

C, Java, Python

Java

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

Linux

hosted

Linux
OS X
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

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

with add-on product Adabas SQL Gateway

Standard with numerous extensions

yes

SQL-like query language (GQL)

APIs and other access methods

HTTP API

with add-on software Adabas SOA Gateway
SOAP-based API

with add-on software Adabas SOA Gateway

ADO.NET
C API
JDBC
ODBC

RESTful HTTP/JSON API

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

HTTP REST

Supported programming languages

Natural

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Go
Java
Python
Ruby

Server-side scripts

Stored procedures

in Natural

user defined functions

in JavaScript

using Google App Engine

Triggers

yes

Callbacks using the Google Apps Engine

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

yes, with additonal products like Adabas Cluster Services, Adabas Parallel Services, Adabas Vista

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

none

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

yes

Multi-source replication using Paxos

selectable replication factor

based on Cassandra

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

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.

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Since BigQuery is designed for querying data

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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.

User concepts

Access control

only with OS-specific tools (e.g. IBM RACF, CA Top Secret)

yes

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

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

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