DBMS > HEAVY.AI vs. IBM Db2 Event Store vs. Netezza vs. Oracle Coherence vs. Snowflake

System Properties Comparison HEAVY.AI vs. IBM Db2 Event Store vs. Netezza vs. Oracle Coherence vs. Snowflake

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

Data warehouse and analytics appliance part of IBM PureSystems

Oracles in-memory data grid solution

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

Primary database model

Relational DBMS

Event Store
Time Series DBMS

Relational DBMS

Key-value store

Relational DBMS

Secondary database models

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	8.59
Rank	#45	Overall
	#29	Relational DBMS

Score	1.97
Rank	#129	Overall
	#22	Key-value stores

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

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

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

www.ibm.com/products/netezza

www.oracle.com/java/coherence

www.snowflake.com

Technical documentation

docs.heavy.ai

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

docs.oracle.com/en/middleware/standalone/coherence

docs.snowflake.net/manuals/index.html

Developer

HEAVY.AI, Inc.

IBM

Oracle

Snowflake Computing Inc.

Initial release

2016

2017

2000

2007

2014

Current release

5.10, January 2022

2.0

14.1, August 2023

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

commercial

free developer 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

C++ and CUDA

C and C++

Java

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

Linux

included in appliance

All OS with a Java VM

hosted

Data scheme

yes

schema-free

yes

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

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

JDBC
ODBC
OLE DB

JCache
JPA
RESTful HTTP API

CLI Client
JDBC
ODBC

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++
Fortran
Java
Lua
Perl
Python
R

.Net
C++
Java

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

Live Events

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Active-active shard replication

Source-replica replication

yes, with selectable consistency level

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

configurable

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

optionally

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

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

authentification to access the cache via certificates or http basic authentication

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

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