DBMS > Bangdb vs. eXtremeDB vs. Microsoft Access vs. Snowflake vs. Splice Machine

System Properties Comparison Bangdb vs. eXtremeDB vs. Microsoft Access vs. Snowflake vs. Splice Machine

Editorial information provided by DB-Engines

Name

Description

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

Natively in-memory DBMS with options for persistency, high-availability and clustering

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

Cloud-based data warehousing service for structured and semi-structured data

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational DBMS
Time Series DBMS

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	0.74
Rank	#223	Overall
	#103	Relational DBMS
	#18	Time Series DBMS

Score	104.92
Rank	#11	Overall
	#8	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

bangdb.com

www.mcobject.com

www.microsoft.com/en-us/microsoft-365/access

www.snowflake.com

splicemachine.com

Technical documentation

docs.bangdb.com

www.mcobject.com/docs/extremedb.htm

developer.microsoft.com/en-us/access

docs.snowflake.net/manuals/index.html

splicemachine.com/how-it-works

Developer

Sachin Sinha, BangDB

McObject

Microsoft

Snowflake Computing Inc.

Splice Machine

Initial release

2012

2001

1992

2014

Current release

BangDB 2.0, October 2021

8.2, 2021

1902 (16.0.11328.20222), March 2019

3.1, March 2021

License

Commercial or Open Source

Open Source

BSD 3

commercial

Bundled with Microsoft Office

commercial

Open Source

AGPL 3.0, 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, C++

C and C++

C++

Java

Server operating systems

Linux

AIX
HP-UX
Linux
macOS
Solaris
Windows

Windows

Not a real database server, but making use of DLLs

hosted

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

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.

support of XML interfaces available

yes

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

yes

with the option: eXtremeSQL

yes

but not compliant to any SQL standard

yes

APIs and other access methods

Proprietary protocol
RESTful HTTP API

.NET Client API
JDBC
JNI
ODBC
Proprietary protocol
RESTful HTTP API

ADO.NET
DAO
ODBC
OLE DB

CLI Client
JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

Supported programming languages

C
C#
C++
Java
Python

.Net
C
C#
C++
Java
Lua
Python
Scala

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

JavaScript (Node.js)
Python

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

yes

since Access 2010 using the ACE-engine

user defined functions

yes

Java

Triggers

yes, Notifications (with Streaming only)

yes

by defining events

yes

since Access 2010 using the ACE-engine

similar concept for controling cloud resources

yes

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

horizontal partitioning / sharding

none

yes

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

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

Active Replication Fabric™ for IoT
Multi-source replication

by means of eXtremeDB Cluster option
Source-replica replication

by means of eXtremeDB High Availability option

none

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

but no files for transaction logging

ACID

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Optimistic (MVCC) and pessimistic (locking) strategies available

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

but no files for transaction logging

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)

a simple user-level security was built in till version Access 2003

Users with fine-grained authorization concept, user roles and pluggable authentication

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

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

With hundreds of customers and over 30 million devices/applications using the product...
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Licensing and pricing models

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