DBMS > Blazegraph vs. Google BigQuery vs. InterSystems Caché vs. Microsoft Azure Synapse Analytics vs. Snowflake

System Properties Comparison Blazegraph vs. Google BigQuery vs. InterSystems Caché vs. Microsoft Azure Synapse Analytics vs. Snowflake

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.

Caché is a deprecated database engine which is substituted with InterSystems IRIS. It therefore is removed from the DB-Engines Ranking.

Description

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

Large scale data warehouse service with append-only tables

A multi-model DBMS and application server

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Graph DBMS
RDF store

Relational DBMS

Key-value store
Object oriented DBMS
Relational DBMS

Relational DBMS

Secondary database models

Document store

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.81
Rank	#213	Overall
	#19	Graph DBMS
	#8	RDF stores

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

blazegraph.com

cloud.google.com/bigquery

www.intersystems.com/products/cache

azure.microsoft.com/services/synapse-analytics

www.snowflake.com

Technical documentation

wiki.blazegraph.com

cloud.google.com/bigquery/docs

docs.intersystems.com

docs.microsoft.com/azure/synapse-analytics

docs.snowflake.net/manuals/index.html

Developer

Blazegraph

Google

InterSystems

Microsoft

Snowflake Computing Inc.

Initial release

2006

2010

1997

2016

2014

Current release

2.1.5, March 2019

2018.1.4, May 2020

License

Commercial or Open Source

Open Source

extended commercial license available

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

C++

Server operating systems

Linux
OS X
Windows

hosted

AIX
HP Open VMS
HP-UX
Linux
OS X
Solaris
Windows

hosted

Data scheme

schema-free

yes

depending on used data model

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

yes

RDF literal 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

SQL

Support of SQL

SPARQL is used as query language

yes

APIs and other access methods

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

RESTful HTTP/JSON API

.NET Client API
JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

CLI Client
JDBC
ODBC

Supported programming languages

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java

C#
Java
PHP

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

yes

Transact SQL

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding, horizontal partitioning

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in Graphs

yes

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

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

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

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access rights for users, groups and roles

yes

Users with fine-grained authorization concept, user roles and pluggable authentication

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