DBMS > GigaSpaces vs. Kdb vs. Microsoft Azure Data Explorer vs. OpenEdge vs. Snowflake

System Properties Comparison GigaSpaces vs. Kdb vs. Microsoft Azure Data Explorer vs. OpenEdge vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

High performance Time Series DBMS

Fully managed big data interactive analytics platform

Application development environment with integrated database management system

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Document store
Object oriented DBMS

Values are user defined objects

Time Series DBMS
Vector DBMS

Relational DBMS

column oriented

Relational DBMS

Secondary database models

Graph DBMS
Search engine

Relational 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	1.03
Rank	#188	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	7.71
Rank	#49	Overall
	#2	Time Series DBMS
	#1	Vector DBMS

Score	3.80
Rank	#81	Overall
	#43	Relational DBMS

Score	3.45
Rank	#85	Overall
	#46	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

www.gigaspaces.com

kx.com

azure.microsoft.com/services/data-explorer

www.progress.com/openedge

www.snowflake.com

Technical documentation

docs.gigaspaces.com/latest/landing.html

code.kx.com

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

documentation.progress.com/output/ua/OpenEdge_latest

docs.snowflake.net/manuals/index.html

Developer

Gigaspaces Technologies

Kx Systems, a division of First Derivatives plc

Microsoft

Progress Software Corporation

Snowflake Computing Inc.

Initial release

2000

kdb was released 2000, kdb+ in 2003

2019

1984

2014

Current release

15.5, September 2020

3.6, May 2018

cloud service with continuous releases

OpenEdge 12.2, March 2020

License

Commercial or Open Source

Open Source

Apache Version 2; Commercial licenses available

commercial

free 32-bit version

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, C++, .Net

Server operating systems

Linux
macOS
Solaris
Windows

Linux
OS X
Solaris
Windows

hosted

AIX
HP-UX
Linux
Solaris
Windows

hosted

Data scheme

schema-free

yes

Fixed schema with schema-less datatypes (dynamic)

yes

support of semi-structured data formats (JSON, XML, Avro)

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.

XML can be used for describing objects metadata

yes

Secondary indexes

yes

table attribute 'grouped'

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-99 for query and DML statements

SQL-like query language (q)

Kusto Query Language (KQL), SQL subset

yes

close to SQL 92

yes

APIs and other access methods

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

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

JDBC
ODBC

CLI Client
JDBC
ODBC

Supported programming languages

.Net
C++
Java
Python
Scala

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

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

Progress proprietary ABL (Advanced Business Language)

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions

Yes, possible languages: KQL, Python, R

yes

user defined functions

Triggers

yes, event driven architecture

yes

with views

yes

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

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

Implicit feature of the cloud service

horizontal partitioning

since Version 11.4

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

Source-replica replication

yes

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

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

similar paradigm used for internal processing

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate 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

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

Role-based access control

rights management via user accounts

Azure Active Directory Authentication

Users and groups

Users with fine-grained authorization concept, user roles and pluggable authentication

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
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Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
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Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
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Key customers

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
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Market metrics

kdb+ performance and reliability proven by our customers in critical infrastructure...
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Licensing and pricing models

upon request
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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.