DBMS > Dgraph vs. DolphinDB vs. Microsoft Azure Data Explorer vs. Microsoft Azure Table Storage vs. Newts

System Properties Comparison Dgraph vs. DolphinDB vs. Microsoft Azure Data Explorer vs. Microsoft Azure Table Storage vs. Newts

Editorial information provided by DB-Engines

Name

Description

Distributed and scalable native Graph DBMS

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

Fully managed big data interactive analytics platform

A Wide Column Store for rapid development using massive semi-structured datasets

Time Series DBMS based on Cassandra

Primary database model

column oriented

Secondary database models

Relational 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	1.48
Rank	#158	Overall
	#15	Graph DBMS

Score	4.23
Rank	#82	Overall
	#6	Time Series DBMS

Score	5.16
Rank	#69	Overall
	#37	Relational DBMS

Score	4.92
Rank	#73	Overall
	#6	Wide column stores

Score	0.00
Rank	#396	Overall
	#42	Time Series DBMS

Website

dgraph.io

www.dolphindb.com

azure.microsoft.com/services/data-explorer

azure.microsoft.com/en-us/services/storage/tables

opennms.github.io/newts

Technical documentation

dgraph.io/docs

docs.dolphindb.cn/en/help200/index.html

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

github.com/OpenNMS/newts/wiki

Developer

Dgraph Labs, Inc.

DolphinDB, Inc

Microsoft

OpenNMS Group

Initial release

2016

2018

2019

2012

2014

Current release

v2.00.4, January 2022

cloud service with continuous releases

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

free community version available

commercial

Open Source

Apache 2.0

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

C++

Java

Server operating systems

Linux
OS X
Windows

Linux
Windows

hosted

Linux
OS X
Windows

Data scheme

schema-free

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

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

SQL

Support of SQL

SQL-like query language

Kusto Query Language (KQL), SQL subset

APIs and other access methods

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

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

RESTful HTTP API

HTTP REST
Java API

Supported programming languages

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

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

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

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java

Server-side scripts

Stored procedures

yes

Yes, possible languages: KQL, Python, R

Triggers

yes

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

Partitioning methods

Methods for storing different data on different nodes

yes

horizontal partitioning

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

yes

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

yes

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

selectable replication factor

based on Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

Transaction concepts

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

ACID

yes

optimistic locking

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

User concepts

Access control

Planned for future releases

Administrators, Users, Groups

Azure Active Directory Authentication

Access rights based on private key authentication or shared access signatures

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