DBMS > Amazon Neptune vs. Badger vs. BigchainDB vs. HEAVY.AI

System Properties Comparison Amazon Neptune vs. Badger vs. BigchainDB vs. HEAVY.AI

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Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

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

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Primary database model

Secondary database models

Spatial DBMS

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

Score	2.58
Rank	#112	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	0.20
Rank	#325	Overall
	#49	Key-value stores

Score	0.82
Rank	#212	Overall
	#36	Document stores

Score	2.10
Rank	#126	Overall
	#61	Relational DBMS

Website

aws.amazon.com/neptune

github.com/dgraph-io/badger

www.bigchaindb.com

github.com/heavyai/heavydb
www.heavy.ai

Technical documentation

aws.amazon.com/neptune/developer-resources

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

bigchaindb.readthedocs.io/en/latest

docs.heavy.ai

Developer

Amazon

DGraph Labs

HEAVY.AI, Inc.

Initial release

2017

2016

Current release

5.10, January 2022

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

AGPL v3

Open Source

Apache Version 2; enterprise edition available

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

Python

C++ and CUDA

Server operating systems

hosted

BSD
Linux
OS X
Solaris
Windows

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

SQL

Support of SQL

yes

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

CLI Client
RESTful HTTP API

JDBC
ODBC
Thrift
Vega

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

Go
Haskell
Java
JavaScript
Python
Ruby

All languages supporting JDBC/ODBC/Thrift
Python

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.

none

selectable replication factor

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

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

with encyption-at-rest

yes

yes,with MongoDB ord RethinkDB

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

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

yes

fine grained access rights according to SQL-standard

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More resources

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