DBMS > FeatureBase vs. Microsoft Azure Data Explorer vs. OrigoDB vs. Riak TS

System Properties Comparison FeatureBase vs. Microsoft Azure Data Explorer vs. OrigoDB vs. Riak TS

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Editorial information provided by DB-Engines

Name

Description

Real-time database platform that powers real-time analytics and machine learning applications by simultaneously executing low-latency, high-throughput, and highly concurrent workloads.

Fully managed big data interactive analytics platform

A fully ACID in-memory object graph database

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Primary database model

Relational DBMS

column oriented

Document store
Object oriented DBMS

Time Series DBMS

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	0.31
Rank	#292	Overall
	#135	Relational DBMS

Score	3.80
Rank	#81	Overall
	#43	Relational DBMS

Score	0.06
Rank	#380	Overall
	#50	Document stores
	#19	Object oriented DBMS

Score	0.28
Rank	#307	Overall
	#27	Time Series DBMS

Website

www.featurebase.com

azure.microsoft.com/services/data-explorer

origodb.com

Technical documentation

docs.featurebase.com

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

origodb.com/docs

www.tiot.jp/riak-docs/riak/ts/latest

Developer

Molecula and Pilosa Open Source Contributors

Microsoft

Robert Friberg et al

Open Source, formerly Basho Technologies

Initial release

2017

2019

2009

under the name LiveDB

2015

Current release

2022, May 2022

cloud service with continuous releases

3.0.0, September 2022

License

Commercial or Open Source

commercial

Open Source

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

Erlang

Server operating systems

Linux
macOS

hosted

Linux
Windows

Linux
OS X

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

User defined using .NET types and collections

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

can be achieved using .NET

Secondary indexes

all fields are automatically indexed

yes

restricted

SQL

Support of SQL

SQL queries

Kusto Query Language (KQL), SQL subset

yes, limited

APIs and other access methods

gRPC
JDBC
Kafka Connector
ODBC

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

.NET Client API
HTTP API
LINQ

HTTP API
Native Erlang Interface

Supported programming languages

Java
Python

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

.Net

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

Yes, possible languages: KQL, Python, R

yes

Erlang

Triggers

yes

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

yes

Domain Events

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

horizontal partitioning

client side managed; servers are not synchronized

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.

Source-replica replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

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

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

depending on model

links between datasets can be stored

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes, using Linux fsync

yes

Write ahead log

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

Azure Active Directory Authentication

Role based authorization

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