DBMS > Apache Druid vs. Google BigQuery vs. Heroic vs. Weaviate

System Properties Comparison Apache Druid vs. Google BigQuery vs. Heroic vs. Weaviate

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Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Large scale data warehouse service with append-only tables

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

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

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Time Series DBMS

Vector DBMS

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

Score	3.25
Rank	#90	Overall
	#47	Relational DBMS
	#7	Time Series DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	1.52
Rank	#153	Overall
	#5	Vector DBMS

Website

druid.apache.org

cloud.google.com/bigquery

github.com/spotify/heroic

github.com/weaviate/weaviate
weaviate.io

Technical documentation

druid.apache.org/docs/latest/design

cloud.google.com/bigquery/docs

spotify.github.io/heroic

weaviate.io/developers/weaviate

Developer

Apache Software Foundation and contributors

Google

Spotify

Weaviate B.V.

Initial release

2012

2010

2014

2019

Current release

29.0.1, April 2024

1.19, May 2023

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

Apache 2.0

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.

Implementation language

Java

Server operating systems

Linux
OS X
Unix

hosted

Data scheme

yes

schema-less columns are supported

yes

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

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 for querying

yes

GraphQL is used as query language

APIs and other access methods

JDBC
RESTful HTTP/JSON API

RESTful HTTP/JSON API

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

GraphQL query language
RESTful HTTP/JSON API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

JavaScript / TypeScript
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Since BigQuery is designed for querying data

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

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

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

Google Cloud Identity & Access Management (IAM)

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,...
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Competitive advantages

Flexible deployment - Free, open source or fully-managed cloud vector database service...
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Typical application scenarios

As a database supporting the development of generative AI and semantic search applications...
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Key customers

All companies that have data.
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Market metrics

As of mid 2023: Over 2 million open source downloads 3500+ Weaviate Slack community...
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Licensing and pricing models

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