DBMS > Badger vs. Heroic vs. SWC-DB vs. Transbase vs. Weaviate

System Properties Comparison Badger vs. Heroic vs. SWC-DB vs. Transbase vs. Weaviate

Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

A high performance, scalable Wide Column DBMS

A resource-optimized, high-performance, universally applicable RDBMS

An AI-native realtime vector database engine that integrates scalable machine learning models.

Primary database model

Secondary database models

Time Series DBMS

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

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Score	0.11
Rank	#341	Overall
	#150	Relational DBMS

Score	1.73
Rank	#143	Overall
	#5	Vector DBMS

Website

github.com/dgraph-io/badger

github.com/spotify/heroic

github.com/kashirin-alex/swc-db
www.swcdb.org

www.transaction.de/en/products/transbase.html

github.com/weaviate/weaviate
weaviate.io

Technical documentation

godoc.org/github.com/dgraph-io/badger

spotify.github.io/heroic

www.transaction.de/en/products/transbase/features.html

weaviate.io/developers/weaviate

Developer

DGraph Labs

Spotify

Alex Kashirin

Transaction Software GmbH

Weaviate B.V.

Initial release

2017

2014

2020

1987

2019

Current release

0.5, April 2021

Transbase 8.3, 2022

1.19, May 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

GPL V3

commercial

free development license

Open Source

commercial license available with Weaviate Enterprise

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

C and C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

schema-free

yes

yes, maps to GraphQL interface

Typing

predefined data types such as float or date

yes

string, int, float, geo point, date, cross reference, fuzzy references

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

via Elasticsearch

yes

all data objects are indexed in a semantic vector space (the Contextionary), all primitive fields are indexed

SQL

Support of SQL

SQL-like query language

yes

GraphQL is used as query language

APIs and other access methods

HQL (Heroic Query Language, a JSON-based language)
HTTP API

Proprietary protocol
Thrift

ADO.NET
JDBC
ODBC
Proprietary native API

GraphQL query language
RESTful HTTP/JSON API

Supported programming languages

C++

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

JavaScript / TypeScript
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

none

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

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

fine grained access rights according to SQL-standard

API Keys
OpenID Connect Discovery

More information provided by the system vendor

Specific characteristics

Weaviate is an open source vector database that is robust, scalable, cloud-native,...
» more

Competitive advantages

Flexible deployment - Free, open source or fully-managed cloud vector database service...
» more

Typical application scenarios

As a database supporting the development of generative AI and semantic search applications...
» more

Key customers

All companies that have data.
» more

Market metrics

As of mid 2023: Over 2 million open source downloads 3500+ Weaviate Slack community...
» more

Licensing and pricing models

Weaviate is open-source, and free to use. Weaviate is also available as a fully 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

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

Weaviate, an ANN Database with CRUD support
2 February 2021, Etienne Dilocker, SeMI Technologies (sponsor)

show all

Recent citations in the news

Review: Google Bigtable scales with ease
7 September 2016, InfoWorld

provided by Google News

2022 All O-Zone Football Team
17 December 2022, Ozarks Sports Zone

provided by Google News

Build enterprise-ready generative AI solutions with Cohere foundation models in Amazon Bedrock and Weaviate vector ...
24 January 2024, AWS Blog

Weaviate Partners with Snowflake to Bring Secure GenAI to Snowpark Container Services
8 February 2024, Datanami

Weaviate Partners with Snowflake to Bring Secure GenAI to Snowpark Container Services
9 February 2024, AiThority

Foley Represents Cortical Ventures in $50M Series B Round for Weaviate
17 December 2023, Foley & Lardner LLP

Getting Started with Weaviate: A Beginner's Guide to Search with Vector Databases
18 July 2023, Towards Data Science

provided by Google News

Share this page