DBMS > Apache Pinot vs. Microsoft Azure Table Storage vs. MonetDB vs. SAP HANA vs. Titan

System Properties Comparison Apache Pinot vs. Microsoft Azure Table Storage vs. MonetDB vs. SAP HANA 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

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

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

A relational database management system that stores data in columns

In-memory, column based data store. Available as appliance or cloud service

Titan is a Graph DBMS optimized for distributed clusters.

Primary database model

Secondary database models

Document store
Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	0.38
Rank	#275	Overall
	#126	Relational DBMS

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	1.72
Rank	#141	Overall
	#64	Relational DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Website

pinot.apache.org

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

www.monetdb.org

www.sap.com/products/hana.html

github.com/thinkaurelius/titan

Technical documentation

docs.pinot.apache.org

www.monetdb.org/Documentation

help.sap.com/hana

github.com/thinkaurelius/titan/wiki

Developer

Apache Software Foundation and contributors

Microsoft

MonetDB BV

SAP

Aurelius, owned by DataStax

Initial release

2015

2012

2004

2010

2012

Current release

1.0.0, September 2023

Dec2023 (11.49), December 2023

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Mozilla Public License 2.0

commercial

Open Source

Apache license, version 2.0

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Server operating systems

All OS with a Java JDK11 or higher

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Appliance or cloud-service

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

SQL 2003 with some extensions

yes

APIs and other access methods

JDBC

RESTful HTTP API

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

JDBC
ODBC

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Supported programming languages

Go
Java
Python

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

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

Clojure
Java
Python

Server-side scripts

Stored procedures

yes, in SQL, C, R

SQLScript, R

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

Implicit feature of the cloud service

Sharding via remote tables

yes

via pluggable storage backends

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

none

Source-replica replication available in experimental status

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

optimistic locking

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.

yes

User concepts

Access control

Access rights based on private key authentication or shared access signatures

fine grained access rights according to SQL-standard

yes

User authentification and security via Rexster Graph Server

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