DBMS > Blazegraph vs. MaxDB vs. Milvus vs. Snowflake vs. Spark SQL

System Properties Comparison Blazegraph vs. MaxDB vs. Milvus vs. Snowflake vs. Spark SQL

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.

A robust and reliable RDBMS optimized to run all major SAP solutions

A DBMS designed for efficient storage of vector data and vector similarity searches

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

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	2.32
Rank	#112	Overall
	#55	Relational DBMS

Score	2.31
Rank	#113	Overall
	#3	Vector DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

Technical documentation

wiki.blazegraph.com

maxdb.sap.com/documentation

milvus.io/docs/overview.md

docs.snowflake.net/manuals/index.html

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Blazegraph

SAP, acquired from Software AG (Adabas-D) in 1997

Snowflake Computing Inc.

Apache Software Foundation

Initial release

2006

1984

2019

2014

Current release

2.1.5, March 2019

7.9.10.12, February 2024

2.3.4, January 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

extended commercial license available

commercial

Limited community edition free

Open Source

Apache Version 2.0

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.

Zilliz Cloud – Cloud-native service for Milvus

Implementation language

Java

C++

C++, Go

Scala

Server operating systems

Linux
OS X
Windows

AIX
HP-UX
Linux
Solaris
Windows

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

hosted

Linux
OS X
Windows

Data scheme

schema-free

yes

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

yes

Typing

predefined data types such as float or date

yes

RDF literal types

yes

Vector, Numeric and String

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

SQL-like DML and DDL statements

APIs and other access methods

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

ADO.NET
JDBC
ODBC
WebDAV

RESTful HTTP API

CLI Client
JDBC
ODBC

JDBC
ODBC

Supported programming languages

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

.Net
C#
Java
Perl
PHP
Python

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

JavaScript (Node.js)
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

yes

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

yes

none

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

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable 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

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)

fine grained access rights according to SQL-standard

Role based access control and fine grained access rights

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

More information provided by the system vendor

Specific characteristics

Milvus is an open-source and cloud-native vector database built for production-ready...
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Competitive advantages

Highly available, versatile, and robust with millisecond latency. Supports batch...
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Typical application scenarios

RAG: retrieval augmented generation Video media : video understanding, video deduplication....
» more

Key customers

Milvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
» more

Market metrics

As of January 2024, 25k+ GitHub stars 10M+ downloads and installations 3k+ enterprise...
» more

Licensing and pricing models

Milvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
» more

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