DBMS > Adabas vs. Amazon SimpleDB vs. Apache Pinot vs. Snowflake vs. Splice Machine

System Properties Comparison Adabas vs. Amazon SimpleDB vs. Apache Pinot vs. Snowflake vs. Splice Machine

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

Hosted simple database service by Amazon, with the data stored in the Amazon Cloud.

There is an unrelated product called SimpleDB developed by Edward Sciore

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

Cloud-based data warehousing service for structured and semi-structured data

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Multivalue DBMS

Key-value store

Relational DBMS

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

Score	2.54
Rank	#104	Overall
	#1	Multivalue DBMS

Score	1.74
Rank	#134	Overall
	#23	Key-value stores

Score	0.36
Rank	#270	Overall
	#126	Relational DBMS

Score	142.50
Rank	#7	Overall
	#5	Relational DBMS

Score	0.54
Rank	#242	Overall
	#113	Relational DBMS

Website

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

aws.amazon.com/simpledb

pinot.apache.org

www.snowflake.com

splicemachine.com

Technical documentation

docs.aws.amazon.com/simpledb

docs.pinot.apache.org

docs.snowflake.net/manuals/index.html

splicemachine.com/how-it-works

Developer

Software AG

Amazon

Apache Software Foundation and contributors

Snowflake Computing Inc.

Splice Machine

Initial release

1971

2007

2015

2014

Current release

1.0.0, September 2023

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Open Source

AGPL 3.0, commercial license 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

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

hosted

All OS with a Java JDK11 or higher

hosted

Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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

All columns are indexed automatically

yes

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

SQL-like query language

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

JDBC

CLI Client
JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

Supported programming languages

Natural

.Net
C
C++
Erlang
Java
PHP
Python
Ruby
Scala

Go
Java
Python

JavaScript (Node.js)
Python

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

in Natural

user defined functions

yes

Java

Triggers

similar concept for controling cloud resources

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

Sharding must be implemented in the application

horizontal partitioning

yes

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrent data updates can be detected by the application

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

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

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

Users with fine-grained authorization concept, user roles and pluggable authentication

Access rights for users, groups and roles according to SQL-standard

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