DBMS > Heroic vs. IBM Cloudant vs. Microsoft Azure Synapse Analytics vs. SAP Adaptive Server

System Properties Comparison Heroic vs. IBM Cloudant vs. Microsoft Azure Synapse Analytics vs. SAP Adaptive Server

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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

Database as a Service offering based on Apache CouchDB

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

The SAP (Sybase) Adaptive Server Enterprise (ASE) is an enterprise-class RDBMS

Primary database model

Time Series DBMS

Document store

Relational DBMS

Secondary database models

Spatial DBMS

with Boeing's Spatial Query Server

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

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	2.68
Rank	#106	Overall
	#20	Document stores

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	36.31
Rank	#25	Overall
	#17	Relational DBMS

Website

github.com/spotify/heroic

www.ibm.com/products/cloudant

azure.microsoft.com/services/synapse-analytics

www.sap.com/products/technology-platform/sybase-ase.html

Technical documentation

spotify.github.io/heroic

cloud.ibm.com/docs/Cloudant

docs.microsoft.com/azure/synapse-analytics

help.sap.com/docs/SAP_ASE

Developer

Spotify

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Microsoft

SAP, Sybase

Initial release

2014

2010

2016

1987

Current release

16.0

License

Commercial or Open Source

Open Source

Apache 2.0

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

Erlang

C++

C and C++

Server operating systems

hosted

AIX
HP-UX
Linux
Solaris
Unix
Windows

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/JSON API

ADO.NET
JDBC
ODBC

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

Supported programming languages

C#
Java
JavaScript
Objective-C
PHP
Ruby

C#
Java
PHP

C
C++
Cobol
Java
Java
Perl
PHP
Python

Server-side scripts

Stored procedures

View functions (Map-Reduce) in JavaScript

Transact SQL

Java and Transact-SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

yes

Transaction concepts

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

atomic operations within a document possible

ACID

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

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.

User concepts

Access control

Access rights for users can be defined per database

yes

fine grained access rights according to SQL-standard

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