DBMS > Hyprcubd vs. Microsoft Azure Data Explorer vs. OpenEdge vs. Oracle Rdb

System Properties Comparison Hyprcubd vs. Microsoft Azure Data Explorer vs. OpenEdge vs. Oracle Rdb

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Editorial information provided by DB-Engines

Name

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

Serverless Time Series DBMS

Fully managed big data interactive analytics platform

Application development environment with integrated database management system

Primary database model

Time Series DBMS

Relational DBMS

column oriented

Relational 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	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	3.51
Rank	#86	Overall
	#46	Relational DBMS

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

Website

hyprcubd.com (offline)

azure.microsoft.com/services/data-explorer

www.progress.com/openedge

www.oracle.com/database/technologies/related/rdb.html

Technical documentation

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

documentation.progress.com/output/ua/OpenEdge_latest

www.oracle.com/database/technologies/related/rdb-doc.html

Developer

Hyprcubd, Inc.

Microsoft

Progress Software Corporation

Oracle, originally developed by Digital Equipment Corporation (DEC)

Initial release

2019

1984

Current release

cloud service with continuous releases

OpenEdge 12.2, March 2020

7.4.1.1, 2021

License

Commercial or Open Source

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

Server operating systems

hosted

AIX
HP-UX
Linux
Solaris
Windows

HP Open VMS

Data scheme

yes

Fixed schema with schema-less datatypes (dynamic)

yes

Flexible Schema (defined schema, partial schema, schema free)

Typing

predefined data types such as float or date

yes

time, int, uint, float, string

yes

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

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

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

Kusto Query Language (KQL), SQL subset

yes

close to SQL 92

yes

APIs and other access methods

gRPC (https)

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

JDBC
ODBC

Supported programming languages

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

Progress proprietary ABL (Advanced Business Language)

Server-side scripts

Stored procedures

Yes, possible languages: KQL, Python, R

yes

Triggers

yes

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

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

horizontal partitioning

since Version 11.4

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.

Source-replica replication

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

Eventual Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes, on a single node

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.

User concepts

Access control

token access

Azure Active Directory Authentication

Users and groups

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