DBMS > Apache Pinot vs. IRONdb vs. Microsoft Azure Synapse Analytics vs. SAP HANA

System Properties Comparison Apache Pinot vs. IRONdb vs. Microsoft Azure Synapse Analytics vs. SAP HANA

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Editorial information provided by DB-Engines

Name

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

Description

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

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

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

Primary database model

Relational DBMS

Time Series DBMS

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	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Website

pinot.apache.org

www.circonus.com/solutions/time-series-database/

azure.microsoft.com/services/synapse-analytics

www.sap.com/products/hana.html

Technical documentation

docs.pinot.apache.org

docs.circonus.com/irondb/category/getting-started

docs.microsoft.com/azure/synapse-analytics

help.sap.com/hana

Developer

Apache Software Foundation and contributors

Circonus LLC.

Microsoft

SAP

Initial release

2015

2017

2016

2010

Current release

1.0.0, September 2023

V0.10.20, January 2018

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

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

Implementation language

Java

C and C++

C++

Server operating systems

All OS with a Java JDK11 or higher

Linux

hosted

Appliance or cloud-service

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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

SQL-like query language (Circonus Analytics Query Language: CAQL)

yes

APIs and other access methods

JDBC

HTTP API

ADO.NET
JDBC
ODBC

JDBC
ODBC

Supported programming languages

Go
Java
Python

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

C#
Java
PHP

Server-side scripts

Stored procedures

yes, in Lua

Transact SQL

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Automatic, metric affinity per node

Sharding, horizontal partitioning

yes

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Foreign keys

Referential integrity

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

yes

Transaction concepts

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

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

yes

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