DBMS > Derby vs. eXtremeDB vs. Microsoft Azure Data Explorer vs. SAP HANA vs. Vertica

System Properties Comparison Derby vs. eXtremeDB vs. Microsoft Azure Data Explorer vs. SAP HANA vs. Vertica

Editorial information provided by DB-Engines

Name

Description

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Natively in-memory DBMS with options for persistency, high-availability and clustering

Fully managed big data interactive analytics platform

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

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Relational DBMS

Relational DBMS
Time Series DBMS

Relational DBMS

column oriented

Relational DBMS

Column oriented

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

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Spatial DBMS
Time Series DBMS

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

Score	4.60
Rank	#70	Overall
	#38	Relational DBMS

Score	0.80
Rank	#214	Overall
	#99	Relational DBMS
	#18	Time Series DBMS

Score	3.80
Rank	#81	Overall
	#43	Relational DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	10.06
Rank	#42	Overall
	#26	Relational DBMS

Website

db.apache.org/derby

www.mcobject.com

azure.microsoft.com/services/data-explorer

www.sap.com/products/hana.html

www.vertica.com

Technical documentation

db.apache.org/derby/manuals/index.html

www.mcobject.com/docs/extremedb.htm

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

help.sap.com/hana

vertica.com/documentation

Developer

Apache Software Foundation

McObject

Microsoft

SAP

OpenText

previously Micro Focus and Hewlett Packard

Initial release

1997

2001

2019

2010

2005

Current release

10.17.1.0, November 2023

8.2, 2021

cloud service with continuous releases

2.0 SPS07 (April 4, 2023), April 2023

12.0.3, January 2023

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C and C++

C++

Server operating systems

All OS with a Java VM

AIX
HP-UX
Linux
macOS
Solaris
Windows

hosted

Appliance or cloud-service

Linux

Data scheme

yes

Fixed schema with schema-less datatypes (dynamic)

yes

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

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

support of XML interfaces available

yes

Secondary indexes

yes

all fields are automatically indexed

yes

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

yes

with the option: eXtremeSQL

Kusto Query Language (KQL), SQL subset

yes

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

JDBC

.NET Client API
JDBC
JNI
ODBC
Proprietary protocol
RESTful HTTP API

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

JDBC
ODBC

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

Java

.Net
C
C#
C++
Java
Lua
Python
Scala

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

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Yes, possible languages: KQL, Python, R

SQLScript, R

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

by defining events

yes

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

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning / sharding

Sharding

Implicit feature of the cloud service

yes

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Active Replication Fabric™ for IoT
Multi-source replication

by means of eXtremeDB Cluster option
Source-replica replication

by means of eXtremeDB High Availability option

yes

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

yes

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

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

Optimistic (MVCC) and pessimistic (locking) strategies available

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

yes

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

eXtremeDB is an in-memory and/or persistent database system that offers an ultra-small...
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Deploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
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Competitive advantages

eXtremeDB databases can be modeled relationally or as objects and can utilize SQL...
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Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenarios

IoT application across all markets: Industrial Control, Netcom, Telecom, Defense,...
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Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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Key customers

Schneider Electronics, F5 Networks, TNS, Boeing, Northrop Grumman, GoPro, ViaSat,...
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Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
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Market metrics

With hundreds of customers and over 30 million devices/applications using the product...
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Licensing and pricing models

For server use cases, there is a simple per-server license irrespective of the number...
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Cost-based models and subscription-based models are both available. One license is...
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