DBMS > Microsoft Azure Table Storage vs. SAP HANA vs. SiteWhere vs. Spark SQL vs. Warp 10

System Properties Comparison Microsoft Azure Table Storage vs. SAP HANA vs. SiteWhere vs. Spark SQL vs. Warp 10

Editorial information provided by DB-Engines

Name

Description

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

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

M2M integration platform for persisting/querying time series data

Spark SQL is a component on top of 'Spark Core' for structured data processing

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

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	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	0.06
Rank	#383	Overall
	#43	Time Series DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.14
Rank	#344	Overall
	#32	Time Series DBMS

Website

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

www.sap.com/products/hana.html

github.com/sitewhere/sitewhere

spark.apache.org/sql

www.warp10.io

Technical documentation

help.sap.com/hana

sitewhere1.sitewhere.io/index.html

spark.apache.org/docs/latest/sql-programming-guide.html

www.warp10.io/content/02_Getting_started

Developer

Microsoft

SAP

SiteWhere

Apache Software Foundation

SenX

Initial release

2012

2010

2014

2015

Current release

2.0 SPS07 (April 4, 2023), April 2023

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Common Public Attribution License Version 1.0

Open Source

Apache 2.0

Open Source

Apache License 2.0

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

Scala

Java

Server operating systems

hosted

Appliance or cloud-service

Linux
OS X
Windows

Data scheme

schema-free

yes

predefined scheme

yes

schema-free

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

yes

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP API

JDBC
ODBC

HTTP REST

JDBC
ODBC

HTTP API
Jupyter
WebSocket

Supported programming languages

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

Java
Python
R
Scala

Server-side scripts

Stored procedures

SQLScript, R

yes

WarpScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

yes

Sharding

based on HBase

yes, utilizing Spark Core

Sharding

based on HBase

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

selectable replication factor

based on HBase

none

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

based on HBase

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

yes

Users with fine-grained authorization concept

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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