DBMS > Cachelot.io vs. Datomic vs. IBM Db2 warehouse vs. Microsoft Azure Synapse Analytics vs. NSDb

System Properties Comparison Cachelot.io vs. Datomic vs. IBM Db2 warehouse vs. Microsoft Azure Synapse Analytics vs. NSDb

Editorial information provided by DB-Engines

Name

Description

In-memory caching system

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

Cloud-based data warehousing service

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

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Primary database model

Key-value store

Relational DBMS

Time Series DBMS

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

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	1.30
Rank	#164	Overall
	#75	Relational DBMS

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

cachelot.io

www.datomic.com

www.ibm.com/products/db2/warehouse

azure.microsoft.com/services/synapse-analytics

nsdb.io

Technical documentation

docs.datomic.com

docs.microsoft.com/azure/synapse-analytics

nsdb.io/Architecture

Developer

Cognitect

IBM

Microsoft

Initial release

2015

2012

2014

2016

2017

Current release

1.0.6735, June 2023

License

Commercial or Open Source

Open Source

Simplified BSD License

commercial

limited edition free

commercial

Open Source

Apache Version 2.0

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++

Java, Clojure

C++

Java, Scala

Server operating systems

FreeBSD
Linux
OS X

All OS with a Java VM

hosted

Linux
macOS

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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.

Import/export of XML data possible

Secondary indexes

yes

all fields are automatically indexed

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

Memcached protocol

RESTful HTTP API

.NET Client API
JDBC
ODBC
OLE DB

ADO.NET
JDBC
ODBC

gRPC
HTTP REST
WebSocket

Supported programming languages

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

Clojure
Java

Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

C#
Java
PHP

Java
Scala

Server-side scripts

Stored procedures

yes

Transaction Functions

PL/SQL, SQL PL

Transact SQL

Triggers

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

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

Using Apache Lucene

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

yes

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