DBMS > Dgraph vs. EsgynDB vs. Firebase Realtime Database vs. Vertica

System Properties Comparison Dgraph vs. EsgynDB vs. Firebase Realtime Database vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

Distributed and scalable native Graph DBMS

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Column oriented

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	1.48
Rank	#158	Overall
	#15	Graph DBMS

Score	0.23
Rank	#319	Overall
	#141	Relational DBMS

Score	15.00
Rank	#39	Overall
	#6	Document stores

Score	11.40
Rank	#43	Overall
	#27	Relational DBMS

Website

dgraph.io

www.esgyn.cn

firebase.google.com/products/realtime-database

www.vertica.com

Technical documentation

dgraph.io/docs

firebase.google.com/docs/database

vertica.com/documentation

Developer

Dgraph Labs, Inc.

Esgyn

Google

acquired by Google 2014

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2016

2015

2012

2005

Current release

12.0.3, January 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++, Java

C++

Server operating systems

Linux
OS X
Windows

Linux

hosted

Linux

Data scheme

schema-free

yes

schema-free

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

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

yes

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

yes

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

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

ADO.NET
JDBC
ODBC

Android
iOS
JavaScript API
RESTful HTTP API

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

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

All languages supporting JDBC/ODBC/ADO.Net

Java
JavaScript
Objective-C

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

Server-side scripts

Stored procedures

Java Stored Procedures

limited functionality with using 'rules'

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

Callbacks are triggered when data changes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

Multi-source replication between multi datacenters

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Immediate Consistency

Foreign keys

Referential integrity

yes

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

In-memory capabilities

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

User concepts

Access control

Planned for future releases

fine grained access rights according to SQL-standard

yes, based on authentication and database rules

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

Deploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more

Competitive advantages

Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
» more

Typical application scenarios

Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more

Key customers

Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more

Licensing and pricing models

Cost-based models and subscription-based models are both available. One license is...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources

DB-Engines blog posts

Cloud-based DBMS's popularity grows at high rates
12 December 2019, Paul Andlinger

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