DBMS > Google BigQuery vs. Heroic vs. IBM Db2 vs. Linter vs. NSDb

System Properties Comparison Google BigQuery vs. Heroic vs. IBM Db2 vs. Linter vs. NSDb

Editorial information provided by DB-Engines

Name

Description

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

RDBMS for high security requirements

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

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

Relational DBMS

Time Series DBMS

Secondary database models

Document store
RDF store

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

with Db2 Spatial Extender

Spatial DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	128.46
Rank	#8	Overall
	#5	Relational DBMS

Score	0.09
Rank	#346	Overall
	#152	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

cloud.google.com/bigquery

github.com/spotify/heroic

www.ibm.com/products/db2

linter.ru

nsdb.io

Technical documentation

cloud.google.com/bigquery/docs

spotify.github.io/heroic

www.ibm.com/docs/en/db2

nsdb.io/Architecture

Developer

Google

Spotify

IBM

relex.ru

Initial release

2010

2014

1983

host version

1990

2017

Current release

12.1, October 2016

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

free version is available

commercial

Open Source

Apache Version 2.0

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 and C++

Java, Scala

Server operating systems

hosted

AIX
HP-UX
Linux
Solaris
Windows
z/OS

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

Linux
macOS

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

via Elasticsearch

yes

all fields are automatically indexed

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

RESTful HTTP/JSON API

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

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

gRPC
HTTP REST
WebSocket

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

Java
Scala

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

proprietary syntax with the possibility to convert from PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

only with Windows/Unix/Linux Version

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

with separate tools (MQ, InfoSphere)

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

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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