DBMS > Bangdb vs. Google Cloud Bigtable vs. Infobright vs. SAP HANA

System Properties Comparison Bangdb vs. Google Cloud Bigtable vs. Infobright vs. SAP HANA

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Editorial information provided by DB-Engines

Name

Description

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

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

In-memory, column based data store. Available as appliance or cloud service

Primary database model

Document store
Graph DBMS
Time Series DBMS

Key-value store
Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
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	2.97
Rank	#92	Overall
	#15	Key-value stores
	#8	Wide column stores

Score	0.88
Rank	#198	Overall
	#92	Relational DBMS

Score	41.32
Rank	#23	Overall
	#16	Relational DBMS

Website

bangdb.com

cloud.google.com/bigtable

ignitetech.com/softwarelibrary/infobrightdb

www.sap.com/products/hana.html

Technical documentation

docs.bangdb.com

cloud.google.com/bigtable/docs

help.sap.com/hana

Developer

Sachin Sinha, BangDB

Google

Ignite Technologies Inc.; formerly InfoBright Inc.

SAP

Initial release

2012

2015

2005

2010

Current release

BangDB 2.0, October 2021

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

BSD 3

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

commercial

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C, C++

Server operating systems

Linux

hosted

Linux
Windows

Appliance or cloud-service

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

Knowledge Grid Technology used instead

yes

SQL

Support of SQL

SQL like support with command line tool

yes

APIs and other access methods

Proprietary protocol
RESTful HTTP API

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

ADO.NET
JDBC
ODBC

JDBC
ODBC

Supported programming languages

C
C#
C++
Java
Python

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

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

SQLScript, R

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

Sharding

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

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

Internal replication in Colossus, and regional replication between two clusters in different zones

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic single-row operations

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)

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

yes

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