DBMS > Badger vs. Datomic vs. gStore vs. Microsoft Azure Synapse Analytics vs. NSDb

System Properties Comparison Badger vs. Datomic vs. gStore vs. Microsoft Azure Synapse Analytics vs. NSDb

Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

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

A native Graph DBMS to store and maintain very large RDF datasets.

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

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

Score	0.22
Rank	#320	Overall
	#47	Key-value stores

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	0.14
Rank	#342	Overall
	#34	Graph DBMS
	#16	RDF stores

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Website

github.com/dgraph-io/badger

www.datomic.com

en.gstore.cn

azure.microsoft.com/services/synapse-analytics

nsdb.io

Technical documentation

godoc.org/github.com/dgraph-io/badger

docs.datomic.com

en.gstore.cn/#/enDocs

docs.microsoft.com/azure/synapse-analytics

nsdb.io/Architecture

Developer

DGraph Labs

Cognitect

Microsoft

Initial release

2017

2012

2016

2017

Current release

1.0.7075, December 2023

1.2, November 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

limited edition free

Open Source

BSD

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

Java, Clojure

C++

Java, Scala

Server operating systems

BSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

Linux

hosted

Linux
macOS

Data scheme

schema-free

yes

schema-free and OWL/RDFS-schema support

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.

Secondary indexes

yes

all fields are automatically indexed

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

RESTful HTTP API

HTTP API
SPARQL 1.1

ADO.NET
JDBC
ODBC

gRPC
HTTP REST
WebSocket

Supported programming languages

Clojure
Java

C++
Java
JavaScript (Node.js)
PHP
Python

C#
Java
PHP

Java
Scala

Server-side scripts

Stored procedures

yes

Transaction Functions

yes

Transact SQL

Triggers

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

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

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

yes

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

User concepts

Access control

Users, roles and permissions, Role-Based Access Control (RBAC) supported

yes

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