DBMS > Google BigQuery vs. Quasardb vs. TimescaleDB vs. Titan vs. Valentina Server

System Properties Comparison Google BigQuery vs. Quasardb vs. TimescaleDB vs. Titan vs. Valentina Server

Editorial information provided by DB-Engines

Name

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

Distributed, high-performance timeseries database

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Titan is a Graph DBMS optimized for distributed clusters.

Object-relational database and reports server

Primary database model

Secondary database models

Relational DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Score	0.17
Rank	#327	Overall
	#145	Relational DBMS

Website

cloud.google.com/bigquery

quasar.ai

www.timescale.com

github.com/thinkaurelius/titan

www.valentina-db.net

Technical documentation

cloud.google.com/bigquery/docs

doc.quasar.ai/master

docs.timescale.com

github.com/thinkaurelius/titan/wiki

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

Google

quasardb

Timescale

Aurelius, owned by DataStax

Paradigma Software

Initial release

2010

2009

2017

2012

1999

Current release

3.14.1, January 2024

2.15.0, May 2024

5.7.5

License

Commercial or Open Source

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

Apache 2.0

Open Source

Apache license, version 2.0

commercial

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++

Java

Server operating systems

hosted

BSD
Linux
OS X
Windows

Linux
OS X
Windows

Linux
OS X
Unix
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

integer and binary

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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.

yes

Secondary indexes

yes

with tags

yes

SQL

Support of SQL

yes

SQL-like query language

yes

full PostgreSQL SQL syntax

yes

APIs and other access methods

RESTful HTTP/JSON API

HTTP API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Clojure
Java
Python

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

user defined functions

in JavaScript

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

consistent hashing

yes, across time and space (hash partitioning) attributes

yes

via pluggable storage backends

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication with selectable replication factor

Source-replica replication with hot standby and reads on replicas

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graph

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

by using LevelDB

yes

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

yes

In-memory capabilities

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

yes

Transient mode

yes

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Cryptographically strong user authentication and audit trail

fine grained access rights according to SQL-standard

User authentification and security via Rexster Graph Server

fine grained access rights according to SQL-standard

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