DBMS > Bangdb vs. Splice Machine vs. Transbase

System Properties Comparison Bangdb vs. Splice Machine vs. Transbase

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

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

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

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

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

Score	0.54
Rank	#244	Overall
	#114	Relational DBMS

Score	0.05
Rank	#355	Overall
	#150	Relational DBMS

Website

bangdb.com

splicemachine.com

www.transaction.de/en/products/transbase.html

Technical documentation

docs.bangdb.com

splicemachine.com/how-it-works

www.transaction.de/en/products/transbase/features.html

Developer

Sachin Sinha, BangDB

Splice Machine

Transaction Software GmbH

Initial release

2012

2014

1987

Current release

BangDB 2.0, October 2021

3.1, March 2021

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

BSD 3

Open Source

AGPL 3.0, commercial license available

commercial

free development license

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C, C++

Java

C and C++

Server operating systems

Linux

Linux
OS X
Solaris
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

schema-free

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.

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

yes

APIs and other access methods

Proprietary protocol
RESTful HTTP API

JDBC
Native Spark Datasource
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C
C#
C++
Java
Python

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

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

yes

Java

yes

Triggers

yes, Notifications (with Streaming only)

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

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)

Multi-source replication
Source-replica 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

yes

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes, multi-version concurrency control (MVCC)

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)

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

fine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Machine learning data pipeline outfit Splice Machine files for insolvency
26 August 2021, The Register

Splice Machine Launches the Splice Machine Feature Store to Simplify Feature Engineering and Democratize Machine Learning
19 January 2021, PR Newswire

Monte Zweben's Splice Machine Introduces New Hadoop RDBMS
24 June 2014, Forbes

Splice Machine Launches Feature Store to Simplify Feature Engineering
19 January 2021, Datanami

Hadoop is not a static data repository, says Splice Machine CEO | #BigDataSV
12 February 2014, SiliconANGLE News

provided by Google News

Share this page