DBMS > Apache Pinot vs. EventStoreDB vs. Microsoft Azure Table Storage vs. SAP HANA vs. TimesTen

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

Editorial information provided by DB-Engines

Name

Description

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

Industrial-strength, open-source database solution built from the ground up for event sourcing.

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

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

In-Memory RDBMS compatible to Oracle

Primary database model

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	0.58
Rank	#244	Overall
	#111	Relational DBMS

Score	1.14
Rank	#181	Overall
	#1	Event Stores

Score	4.92
Rank	#73	Overall
	#6	Wide column stores

Score	45.84
Rank	#22	Overall
	#16	Relational DBMS

Score	1.35
Rank	#165	Overall
	#75	Relational DBMS

Website

pinot.apache.org

www.eventstore.com

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

www.sap.com/products/hana.html

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

docs.pinot.apache.org

developers.eventstore.com

help.sap.com/hana

docs.oracle.com/database/timesten-18.1

Developer

Apache Software Foundation and contributors

Event Store Limited

Microsoft

SAP

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2015

2012

2010

1998

Current release

1.0.0, September 2023

21.2, February 2021

2.0 SPS07 (April 4, 2023), April 2023

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

commercial

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

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Database as a Service

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Implementation language

Java

Server operating systems

All OS with a Java JDK11 or higher

Linux
Windows

hosted

Appliance or cloud-service

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
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

APIs and other access methods

JDBC

RESTful HTTP API

JDBC
ODBC

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

Go
Java
Python

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

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

SQLScript, R

PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

Implicit feature of the cloud service

yes

none

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.

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

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

by means of logfiles and checkpoints

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

yes

fine grained access rights according to SQL-standard

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