DBMS > Amazon Neptune vs. EXASOL vs. Google BigQuery vs. Hive vs. InfluxDB

System Properties Comparison Amazon Neptune vs. EXASOL vs. Google BigQuery vs. Hive vs. InfluxDB

Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

Large scale data warehouse service with append-only tables

data warehouse software for querying and managing large distributed datasets, built on Hadoop

DBMS for storing time series, events and metrics

Primary database model

Graph DBMS
RDF store

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

with GEO package

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	1.99
Rank	#124	Overall
	#58	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	25.83
Rank	#28	Overall
	#1	Time Series DBMS

Website

aws.amazon.com/neptune

www.exasol.com

cloud.google.com/bigquery

hive.apache.org

www.influxdata.com/products/influxdb-overview

Technical documentation

aws.amazon.com/neptune/developer-resources

www.exasol.com/resources

cloud.google.com/bigquery/docs

cwiki.apache.org/confluence/display/Hive/Home

docs.influxdata.com/influxdb

Developer

Amazon

Exasol

Google

Apache Software Foundation

initially developed by Facebook

Initial release

2017

2000

2010

2012

2013

Current release

3.1.3, April 2022

2.7.6, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

MIT-License; commercial enterprise version 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

Java

Server operating systems

hosted

All OS with a Java VM

Linux
OS X

through Homebrew

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

Numeric data and Strings

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

SQL

Support of SQL

yes

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

.Net
JDBC
ODBC
WebSocket

RESTful HTTP/JSON API

JDBC
ODBC
Thrift

HTTP API
JSON over UDP

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

Java
Lua
Python
R

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
PHP
Python

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

user defined functions and integration of map-reduce

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

in enterprise version only

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.

selectable replication factor

in enterprise version only

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with encyption-at-rest

yes

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

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 according to SQL-standard

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access rights for users, groups and roles

simple rights management via user accounts

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