DBMS > Amazon Neptune vs. Google Cloud Bigtable vs. InfluxDB vs. MariaDB vs. NSDb

System Properties Comparison Amazon Neptune vs. Google Cloud Bigtable vs. InfluxDB vs. MariaDB vs. NSDb

Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

DBMS for storing time series, events and metrics

MySQL application compatible open source RDBMS, enhanced with high availability, security, interoperability and performance capabilities. MariaDB ColumnStore provides a column-oriented storage engine and MariaDB Xpand supports distributed SQL.

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

Primary database model

Graph DBMS
RDF store

Key-value store
Wide column store

Time Series DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

with GEO package

Document store
Graph DBMS

with OQGraph storage engine
Spatial DBMS

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

Score	2.20
Rank	#119	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	25.83
Rank	#28	Overall
	#1	Time Series DBMS

Score	93.21
Rank	#13	Overall
	#9	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

aws.amazon.com/neptune

cloud.google.com/bigtable

www.influxdata.com/products/influxdb-overview

mariadb.com

Site of MariaDB Corporation
mariadb.org

Site of MariaDB Foundation

nsdb.io

Technical documentation

aws.amazon.com/neptune/developer-resources

cloud.google.com/bigtable/docs

docs.influxdata.com/influxdb

mariadb.com/kb/en/library

nsdb.io/Architecture

Developer

Amazon

Google

MariaDB Corporation Ab (MariaDB Enterprise),
MariaDB Foundation (community MariaDB Server)

The lead developer Monty Widenius is the original author of MySQL

Initial release

2017

2015

2013

2009

Fork of MySQL, which was first released in 1995

2017

Current release

2.7.6, April 2024

11.3.2, February 2024

License

Commercial or Open Source

commercial

Open Source

MIT-License; commercial enterprise version available

Open Source

GPL version 2, commercial enterprise subscription available

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 and C++

Java, Scala

Server operating systems

hosted

Linux
OS X

through Homebrew

FreeBSD
Linux
Solaris
Windows

ColumnStore storage engine not available on Windows

Linux
macOS

Data scheme

schema-free

yes

Dynamic columns are supported

Typing

predefined data types such as float or date

yes

Numeric data and Strings

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.

yes

Secondary indexes

yes

all fields are automatically indexed

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

SQL-like query language

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

HTTP API
JSON over UDP

ADO.NET
JDBC
ODBC
Proprietary native API

gRPC
HTTP REST
WebSocket

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

C#
C++
Go
Java
JavaScript (Node.js)
Python

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

Ada
C
C#
C++
D
Eiffel
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Java
Scala

Server-side scripts

Stored procedures

yes

PL/SQL compatibility added with version 10.3

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

in enterprise version only

several options for horizontal partitioning and Sharding

Sharding

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.

Internal replication in Colossus, and regional replication between two clusters in different zones

selectable replication factor

in enterprise version only

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

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

Atomic single-row operations

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with encyption-at-rest

yes

not for in-memory storage engine

Using Apache Lucene

In-memory capabilities

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

yes

Depending on used storage engine

yes

with MEMORY storage engine

User concepts

Access control

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

simple rights management via user accounts

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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