DBMS > Amazon Neptune vs. Apache Impala vs. Dgraph vs. Faircom DB vs. Warp 10

System Properties Comparison Amazon Neptune vs. Apache Impala vs. Dgraph vs. Faircom DB vs. Warp 10

Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

Analytic DBMS for Hadoop

Distributed and scalable native Graph DBMS

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Graph DBMS
RDF store

Relational DBMS

Graph DBMS

Key-value store
Relational DBMS

Time Series DBMS

Secondary database models

Document store

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

Score	2.20
Rank	#113	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	10.63
Rank	#40	Overall
	#24	Relational DBMS

Score	1.39
Rank	#155	Overall
	#15	Graph DBMS

Score	0.18
Rank	#314	Overall
	#45	Key-value stores
	#141	Relational DBMS

Score	0.06
Rank	#351	Overall
	#35	Time Series DBMS

Website

aws.amazon.com/neptune

impala.apache.org

dgraph.io

www.faircom.com/products/faircom-db

www.warp10.io

Technical documentation

aws.amazon.com/neptune/developer-resources

impala.apache.org/impala-docs.html

dgraph.io/docs

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

www.warp10.io/content/02_Getting_started

Developer

Amazon

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Dgraph Labs, Inc.

FairCom Corporation

SenX

Initial release

2017

2013

2016

1979

2015

Current release

4.1.0, June 2022

V13, July 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

Apache 2.0

commercial

Restricted, free version available

Open Source

Apache License 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++

ANSI C, C++

Java

Server operating systems

hosted

Linux

Linux
OS X
Windows

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

schema free, schema optional, schema required, partial schema,

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

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

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes, ANSI SQL with proprietary extensions

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

JDBC
ODBC

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

HTTP API
Jupyter
WebSocket

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

All languages supporting JDBC/ODBC

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

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

.Net, JavaScript, C/C++

yes

WarpScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

based on HBase

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

Synchronous replication via Raft

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

ACID

tunable from ACID to Eventually Consistent

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with encyption-at-rest

yes

Yes, tunable from durable to delayed durability to in-memory

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)

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Planned for future releases

Fine grained access rights according to SQL-standard with additional protections for files

Mandatory use of cryptographic tokens, containing fine-grained authorizations

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

How Amazon stores deliver trustworthy shopping and seller experiences using Amazon Neptune
18 September 2024, AWS Blog

Hydrating the Natural History Museum’s Planetary Knowledge Base with Amazon Neptune and Open Data on AWS
13 September 2024, AWS Blog

Using knowledge graphs to build GraphRAG applications with Amazon Bedrock and Amazon Neptune
1 August 2024, AWS Blog

How Prisma Cloud built Infinity Graph using Amazon Neptune and Amazon OpenSearch Service
27 August 2024, AWS Blog

New Amazon Neptune engine version delivers up to 9 times faster and 10 times higher throughput for openCypher query performance
23 July 2024, AWS Blog

provided by Google News

Apache Impala becomes Top-Level Project
28 November 2017, SDTimes.com

Apache Impala 4 Supports Operator Multi-Threading
29 July 2021, iProgrammer

Cloudera brings Apache Iceberg data lake format to its Data Platform
30 June 2022, SiliconANGLE News

Hudi: Uber Engineering’s Incremental Processing Framework on Apache Hadoop
12 March 2017, Uber

Updates & Upserts in Hadoop Ecosystem with Apache Kudu
27 October 2017, KDnuggets

provided by Google News

Top Open Source Graph Databases
3 June 2024, MarkTechPost

Graph Database Market size to grow by USD 7.40 billion between 2022 - 2027 | Growth Driven by Lack of standardization and programming flexibility - Technavio
13 December 2023, Yahoo Finance

Dgraph on AWS: Setting up a horizontally scalable graph database
1 September 2020, AWS Blog

Dgraph raises $11.5 million for scalable graph database solutions
31 July 2019, VentureBeat

provided by Google News

Launched Just A Year Ago, Open-Source Scality S3 Server Gains Extraordinary Momentum As It Simplifies The Life Of Developers
28 June 2017, PR Newswire

provided by Google News

Share this page