DBMS > Apache IoTDB vs. Bangdb vs. Microsoft Azure Data Explorer vs. OrigoDB

System Properties Comparison Apache IoTDB vs. Bangdb vs. Microsoft Azure Data Explorer vs. OrigoDB

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Editorial information provided by DB-Engines

Name

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

Converged and high performance database for device data, events, time series, document and graph

Fully managed big data interactive analytics platform

A fully ACID in-memory object graph database

Primary database model

Time Series DBMS

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

column oriented

Document store
Object oriented DBMS

Secondary database models

Spatial 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	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	3.80
Rank	#81	Overall
	#43	Relational DBMS

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

Website

iotdb.apache.org

bangdb.com

azure.microsoft.com/services/data-explorer

origodb.com

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

docs.bangdb.com

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

origodb.com/docs

Developer

Apache Software Foundation

Sachin Sinha, BangDB

Microsoft

Robert Friberg et al

Initial release

2018

2012

2019

2009

under the name LiveDB

Current release

1.1.0, April 2023

BangDB 2.0, October 2021

cloud service with continuous releases

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

BSD 3

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

Java

C, C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

hosted

Linux
Windows

Data scheme

yes

schema-free

Fixed schema with schema-less datatypes (dynamic)

yes

Typing

predefined data types such as float or date

yes

yes: string, long, double, int, geospatial, stream, events

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

yes

secondary, composite, nested, reverse, geospatial

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

SQL like support with command line tool

Kusto Query Language (KQL), SQL subset

APIs and other access methods

JDBC
Native API

Proprietary protocol
RESTful HTTP API

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

.NET Client API
HTTP API
LINQ

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
C#
C++
Java
Python

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

.Net

Server-side scripts

Stored procedures

yes

Yes, possible languages: KQL, Python, R

yes

Triggers

yes

yes, Notifications (with Streaming only)

yes

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

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Sharding

Implicit feature of the cloud service

horizontal partitioning

client side managed; servers are not synchronized

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

selectable replication factor, Knob for CAP (enterprise version only)

yes

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

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

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

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Tunable consistency, set CAP knob accordingly

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

depending on model

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

yes

Write ahead log

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

yes, run db with in-memory only mode

yes

User concepts

Access control

yes

yes (enterprise version only)

Azure Active Directory Authentication

Role based authorization

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