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

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

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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

Open-Source and Enterprise in-memory Key-Value Store

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

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

Key-value store

Secondary database models

Document store
RDF store

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

with Db2 Spatial Extender

Document store
Search engine

Using distributed Lucene

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

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

Score	0.98
Rank	#196	Overall
	#29	Key-value stores

Website

cloud.google.com/bigquery

github.com/spotify/heroic

www.ibm.com/products/db2

www.alachisoft.com/ncache

Technical documentation

cloud.google.com/bigquery/docs

spotify.github.io/heroic

www.ibm.com/docs/en/db2

www.alachisoft.com/resources/docs

Developer

Google

Spotify

IBM

Alachisoft

Initial release

2010

2014

1983

host version

2005

Current release

12.1, October 2016

5.3.3, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

free version is available

Open Source

Enterprise Edition 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

Java

C and C++

C#, .NET, .NET Core, Java

Server operating systems

hosted

AIX
HP-UX
Linux
Solaris
Windows
z/OS

Linux
Windows

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

partial

Supported data types are Lists, Queues, Hashsets, Dictionary and Counter

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

SQL

Support of SQL

yes

SQL-like query syntax and LINQ for searching the cache. Cache Synchronization with SQL Server using SQL dependency.

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

IDistributedCache
JCache
LINQ
Proprietary native API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

.Net
.Net Core
C#
Java
JavaScript (Node.js)
Python
Scala

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

support for stored procedures with SQL-Server CLR

Triggers

yes

Notifications

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

only with Windows/Unix/Linux Version

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

with separate tools (MQ, InfoSphere)

yes, with selectable consistency level

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Eventual Consistency
Immediate Consistency
Strong Eventual Consistency over WAN with Conflict Resolution using Bridge Topology

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

optimistic locking and pessimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

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

Authentication to access the cache via Active Directory/LDAP (possible roles: user, administrator)

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

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Licensing and pricing models

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