DBMS > Google Cloud Bigtable vs. Oracle vs. Titan vs. Warp 10 vs. Yanza

System Properties Comparison Google Cloud Bigtable vs. Oracle vs. Titan vs. Warp 10 vs. Yanza

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.

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

Description

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Widely used RDBMS

Titan is a Graph DBMS optimized for distributed clusters.

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Time Series DBMS for IoT Applications

Primary database model

Key-value store
Wide column store

Relational DBMS

Graph DBMS

Time Series DBMS

Secondary database models

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Score	0.14
Rank	#344	Overall
	#32	Time Series DBMS

Website

cloud.google.com/bigtable

www.oracle.com/database

github.com/thinkaurelius/titan

www.warp10.io

yanza.com

Technical documentation

cloud.google.com/bigtable/docs

docs.oracle.com/en/database

github.com/thinkaurelius/titan/wiki

www.warp10.io/content/02_Getting_started

Developer

Google

Oracle

Aurelius, owned by DataStax

SenX

Yanza

Initial release

2015

1980

2012

2015

Current release

23c, September 2023

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

Apache license, version 2.0

Open Source

Apache License 2.0

commercial

free version available

Cloud-based only

Only available as a cloud service

yes

but mainly used as a service provided by Yanza

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

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Linux
OS X
Unix
Windows

Linux
OS X
Windows

Windows

Data scheme

schema-free

yes

Schemaless in JSON and XML columns

yes

schema-free

Typing

predefined data types such as float or date

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

SQL

Support of SQL

yes

with proprietary extensions

APIs and other access methods

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

HTTP API
Jupyter
WebSocket

HTTP API

Supported programming languages

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

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

Clojure
Java
Python

any language that supports HTTP calls

Server-side scripts

Stored procedures

PL/SQL

also stored procedures in Java possible

yes

WarpScript

Triggers

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

yes

via pluggable storage backends

Sharding

based on HBase

none

Replication methods

Methods for redundantly storing data on multiple nodes

Internal replication in Colossus, and regional replication between two clusters in different zones

Multi-source replication
Source-replica replication

yes

selectable replication factor

based on HBase

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

can be realized in PL/SQL

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

based on HBase

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graph

Transaction concepts

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

Atomic single-row operations

ACID

isolation level can be parameterized

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

yes

In-memory capabilities

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

yes

Version 12c introduced the new option 'Oracle Database In-Memory'

yes

User concepts

Access control

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

User authentification and security via Rexster Graph Server

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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