DBMS > Google BigQuery vs. Interbase vs. Microsoft Azure Table Storage vs. Microsoft SQL Server vs. Titan

System Properties Comparison Google BigQuery vs. Interbase vs. Microsoft Azure Table Storage vs. Microsoft SQL Server vs. Titan

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

Light-weight proven RDBMS

originally from Borland

A Wide Column Store for rapid development using massive semi-structured datasets

Microsofts flagship relational DBMS

Titan is a Graph DBMS optimized for distributed clusters.

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	4.61
Rank	#72	Overall
	#39	Relational DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Website

cloud.google.com/bigquery

www.embarcadero.com/products/interbase

azure.microsoft.com/en-us/services/storage/tables

www.microsoft.com/en-us/sql-server

github.com/thinkaurelius/titan

Technical documentation

cloud.google.com/bigquery/docs

docs.embarcadero.com/products/interbase

learn.microsoft.com/en-US/sql/sql-server

github.com/thinkaurelius/titan/wiki

Developer

Google

Embarcadero

Microsoft

Aurelius, owned by DataStax

Initial release

2010

1984

2012

1989

2012

Current release

InterBase 2020, December 2019

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

restricted free version is available

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

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Implementation language

C++

Java

Server operating systems

hosted

Android
iOS
Linux
OS X
Windows

hosted

Linux
Windows

Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

yes

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.

export as XML data possible

yes

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C++
Delphi
Java
Object Pascal
PHP
Ruby

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

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Clojure
Java
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Interbase procedure and trigger language

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

tables can be distributed across several files (horizontal partitioning); sharding through federation

yes

via pluggable storage backends

Replication methods

Methods for redundantly storing data on multiple nodes

Interbase Change Views

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes, but depending on the SQL-Server Edition

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

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

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Multiversion concurreny control

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.

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

Access rights based on private key authentication or shared access signatures

fine grained access rights according to SQL-standard

User authentification and security via Rexster Graph Server

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