DBMS > Amazon Redshift vs. GeoSpock vs. Microsoft Azure Data Explorer vs. OrientDB

System Properties Comparison Amazon Redshift vs. GeoSpock vs. Microsoft Azure Data Explorer vs. OrientDB

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Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

Large scale data warehouse service for use with business intelligence tools

Spatial and temporal data processing engine for extreme data scale

Fully managed big data interactive analytics platform

Multi-model DBMS (Document, Graph, Key/Value)

Primary database model

Relational DBMS

column oriented

Document store
Graph DBMS
Key-value store

Secondary database models

Time Series DBMS

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	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	3.19
Rank	#93	Overall
	#16	Document stores
	#7	Graph DBMS
	#14	Key-value stores

Website

aws.amazon.com/redshift

geospock.com

azure.microsoft.com/services/data-explorer

orientdb.org

Technical documentation

docs.aws.amazon.com/redshift

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

www.orientdb.com/docs/last/index.html

Developer

Amazon (based on PostgreSQL)

GeoSpock

Microsoft

OrientDB LTD; CallidusCloud; SAP

Initial release

2012

2019

2010

Current release

2.0, September 2019

cloud service with continuous releases

3.2.29, March 2024

License

Commercial or Open Source

commercial

Open Source

Apache version 2

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, Javascript

Java

Server operating systems

hosted

All OS with a Java JDK (>= JDK 6)

Data scheme

yes

Fixed schema with schema-less datatypes (dynamic)

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

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

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

restricted

temporal, categorical

all fields are automatically indexed

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

ANSI SQL for query only (using Presto)

Kusto Query Language (KQL), SQL subset

SQL-like query language, no joins

APIs and other access methods

JDBC
ODBC

JDBC

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

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

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

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

Server-side scripts

Stored procedures

user defined functions

in Python

Yes, possible languages: KQL, Python, R

Java, Javascript

Triggers

yes

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

Hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic sharding

Sharding

Implicit feature of the cloud service

Sharding

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.

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

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

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

relationship in graphs

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

Access rights for users can be defined per table

Azure Active Directory Authentication

Access rights for users and roles; record level security configurable

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