DBMS > Google BigQuery vs. Microsoft Azure Synapse Analytics vs. Milvus vs. Sphinx vs. WakandaDB

System Properties Comparison Google BigQuery vs. Microsoft Azure Synapse Analytics vs. Milvus vs. Sphinx vs. WakandaDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

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

Open source search engine for searching in data from different sources, e.g. relational databases

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	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	2.31
Rank	#113	Overall
	#3	Vector DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

cloud.google.com/bigquery

azure.microsoft.com/services/synapse-analytics

milvus.io

sphinxsearch.com

wakanda.github.io

Technical documentation

cloud.google.com/bigquery/docs

docs.microsoft.com/azure/synapse-analytics

milvus.io/docs/overview.md

sphinxsearch.com/docs

wakanda.github.io/doc

Developer

Google

Microsoft

Sphinx Technologies Inc.

Wakanda SAS

Initial release

2010

2016

2019

2001

2012

Current release

2.3.4, January 2024

3.5.1, February 2023

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

GPL version 2, commercial licence available

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.

Zilliz Cloud – Cloud-native service for Milvus

Implementation language

C++

C++, Go

C++

C++, JavaScript

Server operating systems

hosted

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

yes

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

full-text index on all search fields

SQL

Support of SQL

yes

SQL-like query language (SphinxQL)

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

RESTful HTTP API

Proprietary protocol

RESTful HTTP API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
Java
PHP

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

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

JavaScript

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Transact SQL

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding, horizontal partitioning

Sharding

Partitioning is done manually, search queries against distributed index is supported

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

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

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

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

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

Google Cloud Identity & Access Management (IAM)

yes

Role based access control and fine grained access rights

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...
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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...
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