DBMS > EXASOL vs. GeoMesa vs. Microsoft Azure Synapse Analytics vs. Milvus vs. WakandaDB

System Properties Comparison EXASOL vs. GeoMesa vs. Microsoft Azure Synapse Analytics vs. Milvus vs. WakandaDB

Editorial information provided by DB-Engines

Name

Description

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

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

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

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

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

Score	3.27
Rank	#97	Overall
	#50	Relational DBMS

Score	0.78
Rank	#199	Overall
	#3	Spatial DBMS

Score	20.66
Rank	#36	Overall
	#22	Relational DBMS

Score	0.37
Rank	#259	Overall
	#17	Search engines

Score	0.02
Rank	#342	Overall
	#18	Object oriented DBMS

Website

www.exasol.com

www.geomesa.org

azure.microsoft.com/services/synapse-analytics

milvus.io

wakanda.github.io

Technical documentation

www.exasol.com/resources

www.geomesa.org/documentation/stable/user/index.html

docs.microsoft.com/azure/synapse-analytics

milvus.io/docs/overview.md

doc.wakanda.org/home2.en.html#/Datastore/Introduction.200-588937.en.html

Developer

Exasol

CCRi and others

Microsoft

Wakanda SAS

Initial release

2000

2014

2016

2019

2012

Current release

3.2.0, April 2021

2.0, January 2022

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

commercial

Open Source

Apache License 2.0

commercial

Open Source

Apache Version 2.0

Open Source

AGPLv3, extended commercial license available

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

Scala

C++

C++, Go

C++, JavaScript

Server operating systems

hosted

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Linux
OS X
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

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.

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

.Net
JDBC
ODBC
WebSocket

ADO.NET
JDBC
ODBC

RESTful HTTP API

Supported programming languages

Java
Lua
Python
R

C#
Java
PHP

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

JavaScript

Server-side scripts

Stored procedures

user defined functions

Transact SQL

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

depending on storage layer

Sharding, horizontal partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

depending on storage layer

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

depending on storage layer

Immediate Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

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

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

depending on storage layer

User concepts

Access control

Access rights for users, groups and roles according to SQL-standard

yes

depending on the DBMS used for storage

yes

More information provided by the system vendor

Specific characteristics

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

Competitive advantages

Highly available, versatile, and robust with millisecond latency. Supports batch...
» more

Typical application scenarios

Video media : video understanding, video deduplication. E-commerce and mobile applications...
» more

Key customers

Milvus is trusted by over 1,000 enterprises, including Baidu, eBay, IKEA, LINE, Shopee,...
» more

Market metrics

As of March 2022, 9.8k+ GitHub stars 1.4k+ Github forks 871k+ docker pulls 2k+ community...
» more

Licensing and pricing models

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

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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