DBMS > Bangdb vs. Google BigQuery vs. Heroic vs. IBM Db2

System Properties Comparison Bangdb vs. Google BigQuery vs. Heroic vs. IBM Db2

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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

Large scale data warehouse service with append-only tables

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Common in IBM host environments, 2 different versions for host and Windows/Linux

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Time Series DBMS

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Secondary database models

Spatial DBMS

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

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

Score	0.15
Rank	#334	Overall
	#45	Document stores
	#31	Graph DBMS
	#31	Time Series DBMS

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	0.57
Rank	#250	Overall
	#21	Time Series DBMS

Score	127.49
Rank	#8	Overall
	#5	Relational DBMS

Website

bangdb.com

cloud.google.com/bigquery

github.com/spotify/heroic

www.ibm.com/products/db2

Technical documentation

docs.bangdb.com

cloud.google.com/bigquery/docs

spotify.github.io/heroic

www.ibm.com/docs/en/db2

Developer

Sachin Sinha, BangDB

Google

Spotify

IBM

Initial release

2012

2010

2014

1983

host version

Current release

BangDB 2.0, October 2021

12.1, October 2016

License

Commercial or Open Source

Open Source

BSD 3

commercial

Open Source

Apache 2.0

commercial

free version is 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++

Java

C and C++

Server operating systems

Linux

hosted

AIX
HP-UX
Linux
Solaris
Windows
z/OS

Data scheme

schema-free

yes

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

via Elasticsearch

yes

SQL

Support of SQL

SQL like support with command line tool

yes

APIs and other access methods

Proprietary protocol
RESTful HTTP API

RESTful HTTP/JSON API

HQL (Heroic Query Language, a JSON-based language)
HTTP API

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

Supported programming languages

C
C#
C++
Java
Python

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

user defined functions

in JavaScript

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

none

Sharding

only with Windows/Unix/Linux Version

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

with separate tools (MQ, InfoSphere)

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying 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

User concepts

Access control

yes (enterprise version only)

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

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