DBMS > Heroic vs. Microsoft Azure AI Search vs. SAP HANA vs. Trafodion

System Properties Comparison Heroic vs. Microsoft Azure AI Search vs. SAP HANA vs. Trafodion

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Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

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

Search-as-a-service for web and mobile app development

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

Transactional SQL-on-Hadoop DBMS

Primary database model

Time Series DBMS

Search engine

Relational DBMS

Secondary database models

Vector 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	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	5.59
Rank	#63	Overall
	#7	Search engines

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Website

github.com/spotify/heroic

azure.microsoft.com/en-us/services/search

www.sap.com/products/hana.html

trafodion.apache.org

Technical documentation

spotify.github.io/heroic

learn.microsoft.com/en-us/azure/search

help.sap.com/hana

trafodion.apache.org/documentation.html

Developer

Spotify

Microsoft

SAP

Apache Software Foundation, originally developed by HP

Initial release

2014

2015

2010

2014

Current release

2.0 SPS07 (April 4, 2023), April 2023

2.3.0, February 2019

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

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

Server operating systems

hosted

Appliance or cloud-service

Linux

Data scheme

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.

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

RESTful HTTP API

JDBC
ODBC

ADO.NET
JDBC
ODBC

Supported programming languages

C#
Java
JavaScript
Python

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

SQLScript, R

Java Stored Procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

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

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

yes

using Azure authentication

yes

fine grained access rights according to SQL-standard

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