DBMS > Adabas vs. EJDB vs. Microsoft Azure Synapse Analytics vs. Spark SQL

System Properties Comparison Adabas vs. EJDB vs. Microsoft Azure Synapse Analytics vs. Spark SQL

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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

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Multivalue DBMS

Document store

Relational DBMS

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

Score	4.78
Rank	#81	Overall
	#1	Multivalue DBMS

Score	0.10
Rank	#314	Overall
	#41	Document stores

Score	20.66
Rank	#36	Overall
	#22	Relational DBMS

Score	22.86
Rank	#35	Overall
	#21	Relational DBMS

Website

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

ejdb.org

azure.microsoft.com/services/synapse-analytics

spark.apache.org/sql

Technical documentation

github.com/Softmotions/ejdb/blob/master/README.md

docs.microsoft.com/azure/synapse-analytics

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Software AG

Softmotions

Microsoft

Apache Software Foundation

Initial release

1971

2012

2016

2014

Current release

3.2.0, October 2021

License

Commercial or Open Source

commercial

Open Source

GPLv2

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++

Scala

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

server-less

hosted

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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.

Secondary indexes

yes

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

yes

SQL-like DML and DDL statements

APIs and other access methods

HTTP API

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

with add-on software Adabas SOA Gateway

in-process shared library

ADO.NET
JDBC
ODBC

JDBC
ODBC

Supported programming languages

Natural

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

C#
Java
PHP

Java
Python
R
Scala

Server-side scripts

Stored procedures

in Natural

Transact SQL

Triggers

Partitioning methods

Methods for storing different data on different nodes

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

none

Sharding, horizontal partitioning

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

none

yes

none

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

typically not needed, however similar functionality with collection joins possible

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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

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