DBMS > Heroic vs. OpenMLDB vs. SAP HANA vs. Warp 10

System Properties Comparison Heroic vs. OpenMLDB vs. SAP HANA vs. Warp 10

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Editorial information provided by DB-Engines

Name

Description

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

An open-source machine learning database that provides a feature platform for training and inference

In-memory, column based data store. Available as appliance or cloud service

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Time Series DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Relational DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	0.10
Rank	#359	Overall
	#36	Time Series DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	0.14
Rank	#344	Overall
	#32	Time Series DBMS

Website

github.com/spotify/heroic

openmldb.ai

www.sap.com/products/hana.html

www.warp10.io

Technical documentation

spotify.github.io/heroic

openmldb.ai/docs/zh/main

help.sap.com/hana

www.warp10.io/content/02_Getting_started

Developer

Spotify

4 Paradigm Inc.

SAP

SenX

Initial release

2014

2020

2010

2015

Current release

2024-2 February 2024

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

commercial

Open Source

Apache License 2.0

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++, Java, Scala

Java

Server operating systems

Linux

Appliance or cloud-service

Linux
OS X
Windows

Data scheme

schema-free

Fixed schema

yes

schema-free

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.

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

yes

APIs and other access methods

HQL (Heroic Query Language, a JSON-based language)
HTTP API

JDBC
SQLAlchemy

JDBC
ODBC

HTTP API
Jupyter
WebSocket

Supported programming languages

C++
Go
Java
Python
Scala

Server-side scripts

Stored procedures

SQLScript, R

yes

WarpScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

yes

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

yes

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

based on HBase

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

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

fine grained access rights according to SQL-standard

yes

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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