DBMS > HyperSQL vs. Microsoft Azure Data Explorer vs. STSdb vs. TempoIQ

System Properties Comparison HyperSQL vs. Microsoft Azure Data Explorer vs. STSdb vs. TempoIQ

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Editorial information provided by DB-Engines

Name

TempoIQ seems to be decommissioned. It will be removed from the DB-Engines ranking.

Description

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

Fully managed big data interactive analytics platform

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Scalable analytics DBMS for sensor data, provided as a service (SaaS)

Primary database model

Relational DBMS

column oriented

Key-value store

Time Series DBMS

Secondary database models

Document store

If a column is of type dynamic docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types/dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store

this is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine

support for complex search expressions docs.microsoft.com/en-us/azure/kusto/query/parseoperator FTS, Geospatial docs.microsoft.com/en-us/azure/kusto/query/geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS

see docs.microsoft.com/en-us/azure/data-explorer/time-series-analysis

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

Score	3.49
Rank	#87	Overall
	#47	Relational DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

hsqldb.org

azure.microsoft.com/services/data-explorer

github.com/STSSoft/STSdb4

tempoiq.com (offline)

Technical documentation

hsqldb.org/web/hsqlDocsFrame.html

docs.microsoft.com/en-us/azure/data-explorer

Developer

Microsoft

STS Soft SC

TempoIQ

Initial release

2001

2019

2011

2012

Current release

2.7.2, June 2023

cloud service with continuous releases

4.0.8, September 2015

License

Commercial or Open Source

Open Source

based on BSD license

commercial

Open Source

GPLv2, commercial license available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

Server operating systems

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

hosted

Windows

Data scheme

yes

Fixed schema with schema-less datatypes (dynamic)

yes

schema-free

Typing

predefined data types such as float or date

yes

bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types

yes

primitive types and user defined types (classes)

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.

yes

Secondary indexes

yes

all fields are automatically indexed

SQL

Support of SQL

yes

Kusto Query Language (KQL), SQL subset

APIs and other access methods

HTTP API

JDBC via HTTP
JDBC
ODBC

Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API

.NET Client API

HTTP API

Supported programming languages

All languages supporting JDBC/ODBC
Java

.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R

C#
Java

C#
Java
JavaScript

Node.js
Python
Ruby

Server-side scripts

Stored procedures

Java, SQL

Yes, possible languages: KQL, Python, R

Triggers

yes

see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy

yes

Realtime Alerts

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

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

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Spark connector (open source): github.com/Azure/azure-kusto-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
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

fine grained access rights according to SQL-standard

Azure Active Directory Authentication

simple authentication-based access control

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