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

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

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Editorial information provided by DB-Engines

Name

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

Data warehouse and analytics appliance part of IBM PureSystems

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

Primary database model

Relational DBMS

Wide column store

Relational DBMS

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	0.40
Rank	#270	Overall
	#125	Relational DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	9.06
Rank	#46	Overall
	#29	Relational DBMS

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Website

pinot.apache.org

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

www.ibm.com/products/netezza

www.sap.com/products/hana.html

Technical documentation

docs.pinot.apache.org

help.sap.com/hana

Developer

Apache Software Foundation and contributors

Microsoft

IBM

SAP

Initial release

2015

2012

2000

2010

Current release

1.0.0, September 2023

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

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

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Implementation language

Java

Server operating systems

All OS with a Java JDK11 or higher

hosted

Linux

included in appliance

Appliance or cloud-service

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
OLE DB

JDBC
ODBC

Supported programming languages

Go
Java
Python

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

C
C++
Fortran
Java
Lua
Perl
Python
R

Server-side scripts

Stored procedures

yes

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

Implicit feature of the cloud service

Sharding

yes

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.

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

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

Users with fine-grained authorization concept

yes

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