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

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

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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

Primary database model

Secondary database models

Document store

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.40
Rank	#270	Overall
	#125	Relational DBMS

Score	1.10
Rank	#179	Overall
	#1	Event Stores

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Website

impala.apache.org

pinot.apache.org

www.eventstore.com

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

www.sap.com/products/hana.html

Technical documentation

impala.apache.org/impala-docs.html

docs.pinot.apache.org

developers.eventstore.com

help.sap.com/hana

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Apache Software Foundation and contributors

Event Store Limited

Microsoft

SAP

Initial release

2013

2015

2012

2010

Current release

4.1.0, June 2022

1.0.0, September 2023

21.2, February 2021

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Apache Version 2

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

C++

Java

Server operating systems

Linux

All OS with a Java JDK11 or higher

Linux
Windows

hosted

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 DML and DDL statements

SQL-like query language

yes

APIs and other access methods

JDBC
ODBC

JDBC

RESTful HTTP API

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

Go
Java
Python

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

Implicit feature of the cloud service

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

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

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

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 for users, groups and roles

based on Apache Sentry and Kerberos

Access rights based on private key authentication or shared access signatures

yes

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