DBMS > BigchainDB vs. Graph Engine vs. Microsoft Azure Data Explorer vs. VoltDB

System Properties Comparison BigchainDB vs. Graph Engine vs. Microsoft Azure Data Explorer vs. VoltDB

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Editorial information provided by DB-Engines

Name

Description

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Fully managed big data interactive analytics platform

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Document store

Graph DBMS
Key-value store

Relational DBMS

column oriented

Relational 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	0.79
Rank	#212	Overall
	#36	Document stores

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

www.bigchaindb.com

www.graphengine.io

azure.microsoft.com/services/data-explorer

www.voltdb.com

Technical documentation

bigchaindb.readthedocs.io/en/latest

www.graphengine.io/docs/manual

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

docs.voltdb.com

Developer

Microsoft

VoltDB Inc.

Initial release

2016

2010

2019

2010

Current release

cloud service with continuous releases

11.3, April 2022

License

Commercial or Open Source

Open Source

AGPL v3

Open Source

MIT License

commercial

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

Python

.NET and C

Java, C++

Server operating systems

Linux

.NET

hosted

Linux
OS X

for development

Data scheme

schema-free

yes

Fixed schema with schema-less datatypes (dynamic)

yes

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

all fields are automatically indexed

yes

SQL

Support of SQL

Kusto Query Language (KQL), SQL subset

yes

only a subset of SQL 99

APIs and other access methods

CLI Client
RESTful HTTP API

RESTful HTTP API

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

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

C#
C++
F#
Visual Basic

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

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

Yes, possible languages: KQL, Python, R

Java

Triggers

yes

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

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

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

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes,with MongoDB ord RethinkDB

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

yes

Snapshots and command logging

In-memory capabilities

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

yes

User concepts

Access control

yes

Azure Active Directory Authentication

Users and roles with access to stored procedures

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