DBMS > Datastax Enterprise vs. IBM Cloudant vs. RocksDB vs. Weaviate

System Properties Comparison Datastax Enterprise vs. IBM Cloudant vs. RocksDB vs. Weaviate

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Editorial information provided by DB-Engines

Name

Description

DataStax Enterprise (DSE) is the always-on, scalable data platform built on Apache Cassandra and designed for hybrid Cloud. DSE integrates graph, search, analytics, administration, developer tooling, and monitoring into a unified platform.

Database as a Service offering based on Apache CouchDB

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

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

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS

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

Score	5.93
Rank	#56	Overall
	#4	Wide column stores

Score	2.75
Rank	#104	Overall
	#19	Document stores

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	1.52
Rank	#153	Overall
	#5	Vector DBMS

Website

www.datastax.com/products/datastax-enterprise

www.ibm.com/products/cloudant

rocksdb.org

github.com/weaviate/weaviate
weaviate.io

Technical documentation

docs.datastax.com

cloud.ibm.com/docs/Cloudant

github.com/facebook/rocksdb/wiki

weaviate.io/developers/weaviate

Developer

DataStax

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Facebook, Inc.

Weaviate B.V.

Initial release

2011

2010

2013

2019

Current release

6.8, April 2020

9.2.1, May 2024

1.19, May 2023

License

Commercial or Open Source

commercial

Open Source

BSD

Open Source

commercial license available with Weaviate Enterprise

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.

Datastax Astra DB: Astra DB simplifies cloud-native Cassandra application development for your apps, microservices and functions. Deploy in minutes on AWS, Google Cloud, Azure, and have it managed for you by the experts, with serverless, pay-as-you-go pricing.

Implementation language

Java

Erlang

C++

Server operating systems

Linux
OS X

hosted

Linux

Data scheme

schema-free

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

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

SQL

Support of SQL

SQL-like DML and DDL statements (CQL); Spark SQL

GraphQL is used as query language

APIs and other access methods

Proprietary protocol

CQL (Cassandra Query Language)
TinkerPop Gremlin

with DSE Graph

RESTful HTTP/JSON API

C++ API
Java API

GraphQL query language
RESTful HTTP/JSON API

Supported programming languages

C
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

C#
Java
JavaScript
Objective-C
PHP
Ruby

C
C++
Go
Java
Perl
Python
Ruby

JavaScript / TypeScript
Python

Server-side scripts

Stored procedures

View functions (Map-Reduce) in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

Sharding

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware, advanced replication for edge computing

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

consistency level can be individually decided with each write operation

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomicity and isolation are supported for single operations

atomic operations within a document possible

yes

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

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

Access rights for users can be defined per object

Access rights for users can be defined per database

API Keys
OpenID Connect Discovery

More information provided by the system vendor

Specific characteristics

DataStax Enterprise is scale-out data infrastructure for enterprises that need to...
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Weaviate is an open source vector database that is robust, scalable, cloud-native,...
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Competitive advantages

Supporting the following application requirements: Zero downtime - Built on Apache...
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Flexible deployment - Free, open source or fully-managed cloud vector database service...
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Typical application scenarios

Applications that must be massively and linearly scalable with 100% uptime and able...
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As a database supporting the development of generative AI and semantic search applications...
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Key customers

Capital One, Cisco, Comcast, eBay, McDonald's, Microsoft, Safeway, Sony, UBS, and...
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All companies that have data.
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Market metrics

Among the Forbes 100 Most Innovative Companies, DataStax is trusted by 5 of the top...
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As of mid 2023: Over 2 million open source downloads 3500+ Weaviate Slack community...
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Licensing and pricing models

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