DBMS > HEAVY.AI vs. IBM Db2 Event Store vs. Microsoft SQL Server vs. Newts vs. Sadas Engine

System Properties Comparison HEAVY.AI vs. IBM Db2 Event Store vs. Microsoft SQL Server vs. Newts vs. Sadas Engine

Editorial information provided by DB-Engines

Name

Description

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

Distributed Event Store optimized for Internet of Things use cases

Microsofts flagship relational DBMS

Time Series DBMS based on Cassandra

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Primary database model

Relational DBMS

Event Store
Time Series DBMS

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.07
Rank	#375	Overall
	#41	Time Series DBMS

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Website

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

www.ibm.com/products/db2-event-store

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

opennms.github.io/newts

www.sadasengine.com

Technical documentation

docs.heavy.ai

www.ibm.com/docs/en/db2-event-store

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

github.com/OpenNMS/newts/wiki

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

HEAVY.AI, Inc.

IBM

Microsoft

OpenNMS Group

SADAS s.r.l.

Initial release

2016

2017

1989

2014

2006

Current release

5.10, January 2022

2.0

SQL Server 2022, November 2022

8.0

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

commercial

free developer edition available

commercial

restricted free version is available

Open Source

Apache 2.0

commercial

free trial version available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++ and CUDA

C and C++

C++

Java

C++

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

Linux
Windows

Linux
OS X
Windows

AIX
Linux
Windows

Data scheme

yes

schema-free

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

through the embedded Spark runtime

yes

APIs and other access methods

JDBC
ODBC
Thrift
Vega

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

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

HTTP REST
Java API

JDBC
ODBC
Proprietary protocol

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

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

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

Java

.Net
C
C#
C++
Groovy
Java
PHP
Python

Server-side scripts

Stored procedures

yes

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

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

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

Sharding

based on Cassandra

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Active-active shard replication

yes, but depending on the SQL-Server Edition

selectable replication factor

based on Cassandra

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

yes

In-memory capabilities

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

yes

managed by 'Learn by Usage'

User concepts

Access control

fine grained access rights according to SQL-standard

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

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