DBMS > Blazegraph vs. Google Cloud Bigtable vs. H2GIS vs. Microsoft Azure Data Explorer

System Properties Comparison Blazegraph vs. Google Cloud Bigtable vs. H2GIS vs. Microsoft Azure Data Explorer

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Editorial information provided by DB-Engines

Name

Amazon has acquired Blazegraph's domain and (probably) product. It is said that Amazon Neptune is based on Blazegraph.

Description

High-performance graph database supporting Semantic Web (RDF/SPARQL) and Graph Database (tinkerpop3, blueprints, vertex-centric) APIs with scale-out and High Availability.

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Spatial extension of H2

Fully managed big data interactive analytics platform

Primary database model

Graph DBMS
RDF store

Key-value store
Wide column store

Spatial DBMS

Relational DBMS

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	0.75
Rank	#219	Overall
	#19	Graph DBMS
	#8	RDF stores

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Website

blazegraph.com

cloud.google.com/bigtable

www.h2gis.org

azure.microsoft.com/services/data-explorer

Technical documentation

wiki.blazegraph.com

cloud.google.com/bigtable/docs

www.h2gis.org/docs/home

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

Developer

Blazegraph

Google

CNRS

Microsoft

Initial release

2006

2015

2013

2019

Current release

2.1.5, March 2019

cloud service with continuous releases

License

Commercial or Open Source

Open Source

extended commercial license available

commercial

Open Source

LGPL 3.0

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

Java

Server operating systems

Linux
OS X
Windows

hosted

Data scheme

schema-free

yes

Fixed schema with schema-less datatypes (dynamic)

Typing

predefined data types such as float or date

yes

RDF literal types

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

all fields are automatically indexed

SQL

Support of SQL

SPARQL is used as query language

yes

Kusto Query Language (KQL), SQL subset

APIs and other access methods

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

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

Supported programming languages

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

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

Java

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

Server-side scripts

Stored procedures

yes

based on H2

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

Sharding

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Internal replication in Colossus, and regional replication between two clusters in different zones

yes

based on H2

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

yes

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in Graphs

yes

Transaction concepts

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

ACID

Atomic single-row operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

Security and Authentication via Web Application Container (Tomcat, Jetty)

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

yes

based on H2

Azure Active Directory Authentication

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