DBMS > Atos Standard Common Repository vs. Bangdb vs. Databricks vs. GridGain

System Properties Comparison Atos Standard Common Repository vs. Bangdb vs. Databricks vs. GridGain

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Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

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

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

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

Primary database model

Document store
Key-value store

Document store
Graph DBMS
Time Series DBMS

Document store
Relational DBMS

Key-value store
Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	78.61
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Website

atos.net/en/convergence-creators/portfolio/standard-common-repository

bangdb.com

www.databricks.com

www.gridgain.com

Technical documentation

docs.bangdb.com

docs.databricks.com

www.gridgain.com/docs/index.html

Developer

Atos Convergence Creators

Sachin Sinha, BangDB

Databricks

GridGain Systems, Inc.

Initial release

2016

2012

2013

2007

Current release

1703

BangDB 2.0, October 2021

GridGain 8.5.1

License

Commercial or Open Source

commercial

Open Source

BSD 3

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

Java

C, C++

Java, C++, .Net

Server operating systems

Linux

hosted

Linux
OS X
Solaris
Windows

Data scheme

Schema and schema-less with LDAP views

schema-free

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

yes

Typing

predefined data types such as float or date

optional

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

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

with Databricks SQL

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

APIs and other access methods

LDAP

Proprietary protocol
RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

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

Supported programming languages

All languages with LDAP bindings

C
C#
C++
Java
Python

Python
R
Scala

C#
C++
Java
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions and aggregates

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

Triggers

yes

yes, Notifications (with Streaming only)

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

yes

yes (replicated cache)

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 or Eventual Consistency depending on configuration

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes

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

yes, run db with in-memory only mode

yes

User concepts

Access control

LDAP bind authentication

yes (enterprise version only)

Security Hooks for custom implementations

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
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