DBMS > Bangdb vs. Dgraph vs. GridGain vs. Teradata Aster

System Properties Comparison Bangdb vs. Dgraph vs. GridGain vs. Teradata Aster

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Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

Converged and high performance database for device data, events, time series, document and graph

Distributed and scalable native Graph DBMS

GridGain is an in-memory computing platform, built on Apache Ignite

Platform for big data analytics on multistructured data sources and types

Primary database model

Document store
Graph DBMS
Time Series DBMS

Graph DBMS

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.07
Rank	#346	Overall
	#47	Document stores
	#35	Graph DBMS
	#32	Time Series DBMS

Score	1.39
Rank	#155	Overall
	#15	Graph DBMS

Score	1.48
Rank	#150	Overall
	#1	Columnar
	#26	Key-value stores
	#2	Object oriented DBMS
	#69	Relational DBMS

Website

bangdb.com

dgraph.io

www.gridgain.com

Technical documentation

docs.bangdb.com

dgraph.io/docs

www.gridgain.com/docs/index.html

Developer

Sachin Sinha, BangDB

Dgraph Labs, Inc.

GridGain Systems, Inc.

Teradata

Initial release

2012

2016

2007

2005

Current release

BangDB 2.0, October 2021

GridGain 8.5.1

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache 2.0

commercial, open source

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

Java, C++, .Net, Python, REST, SQL

Server operating systems

Linux

Linux
OS X
Windows

Linux
OS X
Solaris
Windows
z/OS

Linux

Data scheme

schema-free

yes

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

defined schema within the relational store; partial schema or schema free in the Aster File Store

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

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

in Aster File Store

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

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

yes

APIs and other access methods

Proprietary protocol
RESTful HTTP API

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

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

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

C
C#
C++
Java
Python

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

C#
C++
Java
PHP
Python
Ruby
Scala

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

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

R packages

Triggers

yes, Notifications (with Streaming only)

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

Synchronous replication via Raft

yes (replicated cache)

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

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

yes, run db with in-memory only mode

yes

User concepts

Access control

yes (enterprise version only)

Planned for future releases

Role-based access control
Security Hooks for custom implementations

fine grained access rights according to SQL-standard

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