DBMS > Google BigQuery vs. Hyprcubd vs. IRONdb vs. SiteWhere vs. Titan

System Properties Comparison Google BigQuery vs. Hyprcubd vs. IRONdb vs. SiteWhere vs. Titan

Editorial information provided by DB-Engines

Name

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Titan has been decommisioned after the takeover by Datastax. It will be removed from the DB-Engines ranking. A fork has been open-sourced as JanusGraph.

Description

Large scale data warehouse service with append-only tables

Serverless Time Series DBMS

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

M2M integration platform for persisting/querying time series data

Titan is a Graph DBMS optimized for distributed clusters.

Primary database model

Relational DBMS

Time Series DBMS

Graph DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.06
Rank	#383	Overall
	#43	Time Series DBMS

Website

cloud.google.com/bigquery

hyprcubd.com (offline)

www.circonus.com/solutions/time-series-database/

github.com/sitewhere/sitewhere

github.com/thinkaurelius/titan

Technical documentation

cloud.google.com/bigquery/docs

docs.circonus.com/irondb/category/getting-started

sitewhere1.sitewhere.io/index.html

github.com/thinkaurelius/titan/wiki

Developer

Google

Hyprcubd, Inc.

Circonus LLC.

SiteWhere

Aurelius, owned by DataStax

Initial release

2010

2017

2010

2012

Current release

V0.10.20, January 2018

License

Commercial or Open Source

commercial

Open Source

Common Public Attribution License Version 1.0

Open Source

Apache license, 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

C and C++

Java

Server operating systems

hosted

Linux

Linux
OS X
Windows

Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

predefined scheme

yes

Typing

predefined data types such as float or date

yes

time, int, uint, float, string

yes

text, numeric, histograms

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

SQL

Support of SQL

yes

SQL-like query language

SQL-like query language (Circonus Analytics Query Language: CAQL)

APIs and other access methods

RESTful HTTP/JSON API

gRPC (https)

HTTP API

HTTP REST

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

Clojure
Java
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes, in Lua

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Automatic, metric affinity per node

Sharding

based on HBase

yes

via pluggable storage backends

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware

selectable replication factor

based on HBase

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graph

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

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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)

token access

Users with fine-grained authorization concept

User authentification and security via Rexster Graph Server

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.