DBMS > Axibase vs. Datomic vs. Heroic vs. Microsoft Azure Synapse Analytics

System Properties Comparison Axibase vs. Datomic vs. Heroic vs. Microsoft Azure Synapse Analytics

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Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

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

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

Primary database model

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

Score	0.21
Rank	#308	Overall
	#25	Time Series DBMS

Score	1.55
Rank	#144	Overall
	#67	Relational DBMS

Score	0.13
Rank	#335	Overall
	#29	Time Series DBMS

Score	18.98
Rank	#31	Overall
	#19	Relational DBMS

Website

axibase.com/docs/atsd/finance

www.datomic.com

github.com/spotify/heroic

azure.microsoft.com/services/synapse-analytics

Technical documentation

docs.datomic.com

spotify.github.io/heroic

docs.microsoft.com/azure/synapse-analytics

Developer

Axibase Corporation

Cognitect

Spotify

Microsoft

Initial release

2013

2012

2014

2016

Current release

15585

1.0.7180, July 2024

License

Commercial or Open Source

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

commercial

limited edition free

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

Java, Clojure

Java

C++

Server operating systems

Linux

All OS with a Java VM

hosted

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

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

SQL-like query language

yes

APIs and other access methods

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

RESTful HTTP API

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

ADO.NET
JDBC
ODBC

Supported programming languages

Go
Java
PHP
Python
R
Ruby

Clojure
Java

C#
Java
PHP

Server-side scripts

Stored procedures

yes

Transaction Functions

Transact SQL

Triggers

yes

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

But extensive use of caching in the application peers

Sharding

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

But extensive use of caching in the application peers

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
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

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

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

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

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

yes

recommended only for testing and development

User concepts

Access control

yes

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