DBMS > Apache Impala vs. Apache IoTDB vs. MaxDB vs. SAP HANA

System Properties Comparison Apache Impala vs. Apache IoTDB vs. MaxDB vs. SAP HANA

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

A robust and reliable RDBMS optimized to run all major SAP solutions

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

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	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	2.32
Rank	#112	Overall
	#55	Relational DBMS

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Website

impala.apache.org

iotdb.apache.org

maxdb.sap.com

www.sap.com/products/hana.html

Technical documentation

impala.apache.org/impala-docs.html

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

maxdb.sap.com/documentation

help.sap.com/hana

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Apache Software Foundation

SAP, acquired from Software AG (Adabas-D) in 1997

SAP

Initial release

2013

2018

1984

2010

Current release

4.1.0, June 2022

1.1.0, April 2023

7.9.10.12, February 2024

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2.0

commercial

Limited community edition free

commercial

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

C++

Server operating systems

Linux

All OS with a Java VM (>= 1.8)

AIX
HP-UX
Linux
Solaris
Windows

Appliance or cloud-service

Data scheme

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
Native API

ADO.NET
JDBC
ODBC
WebDAV

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Go
Java
Python
Scala

.Net
C#
Java
Perl
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Foreign keys

Referential integrity

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

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

yes

fine grained access rights according to SQL-standard

yes

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