DBMS > Apache Phoenix vs. EventStoreDB vs. Ingres vs. Microsoft Azure Data Explorer vs. Yanza

System Properties Comparison Apache Phoenix vs. EventStoreDB vs. Ingres vs. Microsoft Azure Data Explorer vs. Yanza

Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

A scale-out RDBMS with evolutionary schema built on Apache HBase

Industrial-strength, open-source database solution built from the ground up for event sourcing.

Well established RDBMS

Fully managed big data interactive analytics platform

Time Series DBMS for IoT Applications

Primary database model

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

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

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	1.10
Rank	#179	Overall
	#1	Event Stores

Score	4.11
Rank	#81	Overall
	#44	Relational DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Website

phoenix.apache.org

www.eventstore.com

www.actian.com/databases/ingres

azure.microsoft.com/services/data-explorer

yanza.com

Technical documentation

phoenix.apache.org

developers.eventstore.com

docs.actian.com/ingres

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

Developer

Apache Software Foundation

Event Store Limited

Actian Corporation

Microsoft

Yanza

Initial release

2014

2012

1974

originally developed at University Berkely in early 1970s

2019

2015

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

21.2, February 2021

11.2, May 2022

cloud service with continuous releases

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

commercial

free version available

Cloud-based only

Only available as a cloud service

yes

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

Server operating systems

Linux
Unix
Windows

Linux
Windows

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

hosted

Windows

Data scheme

yes

late-bound, schema-on-read capabilities

yes

Fixed schema with schema-less datatypes (dynamic)

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

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.

but tools for importing/exporting data from/to XML-files available

yes

Secondary indexes

yes

all fields are automatically indexed

SQL

Support of SQL

yes

Kusto Query Language (KQL), SQL subset

APIs and other access methods

JDBC

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

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

HTTP API

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

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

any language that supports HTTP calls

Server-side scripts

Stored procedures

user defined functions

yes

Yes, possible languages: KQL, Python, R

Triggers

yes

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

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Ingres Star to access multiple databases simultaneously

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Ingres Replicator

yes

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

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

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

MVCC

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

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

fine grained access rights according to SQL-standard

Azure Active Directory Authentication

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More resources

DB-Engines blog posts

Cloudera's HBase PaaS offering now supports Complex Transactions
11 August 2021, Krishna Maheshwari (sponsor)

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