DBMS > HEAVY.AI vs. Kinetica vs. Microsoft Azure Data Explorer vs. SAP HANA vs. searchxml

System Properties Comparison HEAVY.AI vs. Kinetica vs. Microsoft Azure Data Explorer vs. SAP HANA vs. searchxml

Editorial information provided by DB-Engines

Name

Description

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Fully vectorized database across both GPUs and CPUs

Fully managed big data interactive analytics platform

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

DBMS for structured and unstructured content wrapped with an application server

Primary database model

Relational DBMS

column oriented

Relational DBMS

Native XML DBMS
Search engine

Secondary database models

Spatial DBMS

Spatial DBMS
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

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	0.00
Rank	#383	Overall
	#7	Native XML DBMS
	#25	Search engines

Website

github.com/heavyai/heavydb
www.heavy.ai

www.kinetica.com

azure.microsoft.com/services/data-explorer

www.sap.com/products/hana.html

www.searchxml.net/category/products

Technical documentation

docs.heavy.ai

docs.kinetica.com

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

help.sap.com/hana

www.searchxml.net/support/handouts

Developer

HEAVY.AI, Inc.

Kinetica

Microsoft

SAP

informationpartners gmbh

Initial release

2016

2012

2019

2010

2015

Current release

5.10, January 2022

7.1, August 2021

cloud service with continuous releases

2.0 SPS07 (April 4, 2023), April 2023

1.0

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

commercial

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

C++ and CUDA

C, C++

C++

Server operating systems

Linux

hosted

Appliance or cloud-service

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

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

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

Kusto Query Language (KQL), SQL subset

yes

APIs and other access methods

JDBC
ODBC
Thrift
Vega

JDBC
ODBC
RESTful HTTP API

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

JDBC
ODBC

RESTful HTTP API
WebDAV
XQuery
XSLT

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

C++
Java
JavaScript (Node.js)
Python

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

C++

most other programming languages supported via APIs

Server-side scripts

Stored procedures

user defined functions

Yes, possible languages: KQL, Python, R

SQLScript, R

yes

on the application server

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

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

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

Implicit feature of the cloud service

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Source-replica replication

yes

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

yes

sychronisation to multiple collections

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

Immediate Consistency or Eventual Consistency depending on configuration

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

multiple readers, single writer

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

GPU vRAM or System RAM

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles on table level

Azure Active Directory Authentication

yes

Domain, group and role-based access control at the document level and for application services

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