DBMS > Dgraph vs. Google BigQuery vs. Ignite vs. Sadas Engine vs. Transwarp StellarDB

System Properties Comparison Dgraph vs. Google BigQuery vs. Ignite vs. Sadas Engine vs. Transwarp StellarDB

Editorial information provided by DB-Engines

Name

Description

Distributed and scalable native Graph DBMS

Large scale data warehouse service with append-only tables

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

A distributed graph DBMS built for enterprise-level graph applications

Primary database model

Graph DBMS

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Graph DBMS

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

Score	1.53
Rank	#152	Overall
	#15	Graph DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	3.11
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Score	0.07
Rank	#371	Overall
	#39	Graph DBMS

Website

dgraph.io

cloud.google.com/bigquery

ignite.apache.org

www.sadasengine.com

www.transwarp.cn/en/product/stellardb

Technical documentation

dgraph.io/docs

cloud.google.com/bigquery/docs

apacheignite.readme.io/docs

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

Dgraph Labs, Inc.

Google

Apache Software Foundation

SADAS s.r.l.

Transwarp

Initial release

2016

2010

2015

2006

Current release

Apache Ignite 2.6

8.0

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache 2.0

commercial

free trial version available

commercial

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, .Net

C++

Server operating systems

Linux
OS X
Windows

hosted

Linux
OS X
Solaris
Windows

AIX
Linux
Windows

Data scheme

schema-free

yes

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

ANSI-99 for query and DML statements, subset of DDL

yes

SQL-like query language

APIs and other access methods

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

RESTful HTTP/JSON API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

JDBC
ODBC
Proprietary protocol

OpenCypher

Supported programming languages

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

.Net
C
C#
C++
Groovy
Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes (compute grid and cache interceptors can be used instead)

Triggers

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

yes

none

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

yes (replicated cache)

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

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

managed by 'Learn by Usage'

User concepts

Access control

Planned for future releases

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

Google Cloud Identity & Access Management (IAM)

Security Hooks for custom implementations

Access rights for users, groups and roles according to SQL-standard

yes

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