DBMS > Adabas vs. Apache Druid vs. HEAVY.AI vs. Snowflake vs. TigerGraph

System Properties Comparison Adabas vs. Apache Druid vs. HEAVY.AI vs. Snowflake vs. TigerGraph

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

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

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

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Primary database model

Multivalue DBMS

Relational DBMS
Time Series DBMS

Relational DBMS

Graph DBMS

Secondary database models

Spatial DBMS

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

Score	3.58
Rank	#91	Overall
	#1	Multivalue DBMS

Score	3.29
Rank	#95	Overall
	#49	Relational DBMS
	#7	Time Series DBMS

Score	2.10
Rank	#126	Overall
	#61	Relational DBMS

Score	123.20
Rank	#9	Overall
	#6	Relational DBMS

Score	1.83
Rank	#141	Overall
	#13	Graph DBMS

Website

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

druid.apache.org

github.com/heavyai/heavydb
www.heavy.ai

www.snowflake.com

www.tigergraph.com

Technical documentation

druid.apache.org/docs/latest/design

docs.heavy.ai

docs.snowflake.net/manuals/index.html

docs.tigergraph.com

Developer

Software AG

Apache Software Foundation and contributors

HEAVY.AI, Inc.

Snowflake Computing Inc.

Initial release

1971

2012

2016

2014

2017

Current release

29.0.1, April 2024

5.10, January 2022

License

Commercial or Open Source

commercial

Open Source

Apache license v2

Open Source

Apache Version 2; enterprise edition available

commercial

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

C++ and CUDA

C++

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

Linux
OS X
Unix

Linux

hosted

Linux

Data scheme

yes

schema-less columns are supported

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

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

SQL for querying

yes

SQL-like query language (GSQL)

APIs and other access methods

HTTP API

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

with add-on software Adabas SOA Gateway

JDBC
RESTful HTTP/JSON API

JDBC
ODBC
Thrift
Vega

CLI Client
JDBC
ODBC

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

Supported programming languages

Natural

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

All languages supporting JDBC/ODBC/Thrift
Python

JavaScript (Node.js)
Python

C++
Java

Server-side scripts

Stored procedures

in Natural

user defined functions

yes

Triggers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

manual/auto, time-based

Sharding

Round robin

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

yes, via HDFS, S3 or other storage engines

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

ACID

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

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

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

fine grained access rights according to SQL-standard

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

Role-based access control

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