DBMS > Adabas vs. Datomic vs. Google BigQuery vs. Microsoft SQL Server

System Properties Comparison Adabas vs. Datomic vs. Google BigQuery vs. Microsoft SQL Server

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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

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Large scale data warehouse service with append-only tables

Microsofts flagship relational DBMS

Primary database model

Multivalue DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	3.17
Rank	#94	Overall
	#1	Multivalue DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Website

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

www.datomic.com

cloud.google.com/bigquery

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

Technical documentation

docs.datomic.com

cloud.google.com/bigquery/docs

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

Developer

Software AG

Cognitect

Google

Microsoft

Initial release

1971

2012

2010

1989

Current release

1.0.6735, June 2023

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

limited edition free

commercial

restricted free version is available

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, Clojure

C++

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

All OS with a Java VM

hosted

Linux
Windows

Data scheme

yes

Typing

predefined data types such as float or date

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

yes

APIs and other access methods

HTTP API

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

with add-on software Adabas SOA Gateway

RESTful HTTP API

RESTful HTTP/JSON API

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

Supported programming languages

Natural

Clojure
Java

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

in Natural

yes

Transaction Functions

user defined functions

in JavaScript

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

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

none

But extensive use of caching in the application peers

none

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

none

But extensive use of caching in the application peers

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

yes

User concepts

Access control

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

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

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