DBMS > Graph Engine vs. Heroic vs. Microsoft Azure Data Explorer

System Properties Comparison Graph Engine vs. Heroic vs. Microsoft Azure Data Explorer

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Editorial information provided by DB-Engines

Name

Description

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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Fully managed big data interactive analytics platform

Primary database model

Graph DBMS
Key-value store

Time Series DBMS

Relational DBMS

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

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Website

www.graphengine.io

github.com/spotify/heroic

azure.microsoft.com/services/data-explorer

Technical documentation

www.graphengine.io/docs/manual

spotify.github.io/heroic

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

Developer

Microsoft

Spotify

Microsoft

Initial release

2010

2014

2019

Current release

cloud service with continuous releases

License

Commercial or Open Source

Open Source

MIT License

Open Source

Apache 2.0

commercial

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

.NET and C

Java

Server operating systems

.NET

hosted

Data scheme

yes

schema-free

Fixed schema with schema-less datatypes (dynamic)

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.

yes

Secondary indexes

yes

via Elasticsearch

all fields are automatically indexed

SQL

Support of SQL

Kusto Query Language (KQL), SQL subset

APIs and other access methods

RESTful HTTP API

HQL (Heroic Query Language, a JSON-based language)
HTTP API

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

Supported programming languages

C#
C++
F#
Visual Basic

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

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

horizontal partitioning

Sharding

Implicit feature of the cloud service

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.

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

Transaction concepts

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

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

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More resources

Recent citations in the news

Trinity
2 June 2023, Microsoft

Open source Microsoft Graph Engine takes on Neo4j
13 February 2017, InfoWorld

IBM releases Graph, a service that can outperform SQL databases
27 July 2016, GeekWire

How Google and Microsoft taught search to "understand" the Web
6 June 2012, Ars Technica

Aerospike Is Now a Graph Database, Too
21 June 2023, Datanami

provided by Google News

Review: Google Bigtable scales with ease
7 September 2016, InfoWorld

provided by Google News

Azure Data Explorer: Log and telemetry analytics benchmark
16 August 2022, Microsoft

Introducing Microsoft Fabric: The data platform for the era of AI | Microsoft Azure Blog
23 May 2023, Microsoft

Providing modern data transfer and storage service at Microsoft with Microsoft Azure - Inside Track Blog
13 July 2023, Microsoft

Azure Data Explorer and Stream Analytics for anomaly detection
16 January 2020, Microsoft

Controlling costs in Azure Data Explorer using down-sampling and aggregation
11 February 2019, Microsoft

provided by Google News

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