DBMS > Dgraph vs. EsgynDB vs. Google BigQuery vs. IBM Db2 warehouse vs. YDB

System Properties Comparison Dgraph vs. EsgynDB vs. Google BigQuery vs. IBM Db2 warehouse vs. YDB

Editorial information provided by DB-Engines

Name

Description

Distributed and scalable native Graph DBMS

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

Large scale data warehouse service with append-only tables

Cloud-based data warehousing service

A distributed fault-tolerant database service, with high availability, scalability, immediate consistency and ACID transactions and providing an Amazon DynamoDB compatible API

Primary database model

Graph DBMS

Relational DBMS

Document store
Relational DBMS

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

Score	1.53
Rank	#152	Overall
	#15	Graph DBMS

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1.37
Rank	#160	Overall
	#74	Relational DBMS

Score	0.33
Rank	#287	Overall
	#43	Document stores
	#132	Relational DBMS

Website

dgraph.io

www.esgyn.cn

cloud.google.com/bigquery

www.ibm.com/products/db2/warehouse

github.com/ydb-platform/ydb
ydb.tech

Technical documentation

dgraph.io/docs

cloud.google.com/bigquery/docs

ydb.tech/en/docs

Developer

Dgraph Labs, Inc.

Esgyn

Google

IBM

Yandex

Initial release

2016

2015

2010

2014

2019

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache 2.0; commercial license 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

C++, Java

Server operating systems

Linux
OS X
Windows

Linux

hosted

Linux

Data scheme

schema-free

yes

Flexible Schema (defined schema, partial schema, schema free)

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.

Import/export of XML data possible

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language (YQL)

APIs and other access methods

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

ADO.NET
JDBC
ODBC

RESTful HTTP/JSON API

.NET Client API
JDBC
ODBC
OLE DB

RESTful HTTP API (DynamoDB compatible)

Supported programming languages

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

All languages supporting JDBC/ODBC/ADO.Net

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

Go
Java
JavaScript (Node.js)
PHP
Python

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions

in JavaScript

PL/SQL, SQL PL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

Multi-source replication between multi datacenters

yes

Active-passive shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

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.

yes

User concepts

Access control

Planned for future releases

fine grained access rights according to SQL-standard

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

Access rights defined for Yandex Cloud users

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