DBMS > Google BigQuery vs. Google Cloud Datastore vs. Snowflake vs. Tarantool vs. Weaviate

System Properties Comparison Google BigQuery vs. Google Cloud Datastore vs. Snowflake vs. Tarantool vs. Weaviate

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Cloud-based data warehousing service for structured and semi-structured data

In-memory computing platform with a flexible data schema for efficiently building high-performance applications

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

Primary database model

Relational DBMS

Document store

Relational DBMS

Document store
Key-value store
Relational DBMS

Vector DBMS

Secondary database models

Spatial DBMS

with Tarantool/GIS extension

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

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	4.13
Rank	#71	Overall
	#12	Document stores

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Score	1.63
Rank	#139	Overall
	#25	Document stores
	#24	Key-value stores
	#65	Relational DBMS

Score	1.48
Rank	#149	Overall
	#8	Vector DBMS

Website

cloud.google.com/bigquery

cloud.google.com/datastore

www.snowflake.com

www.tarantool.io

github.com/weaviate/weaviate
weaviate.io

Technical documentation

cloud.google.com/bigquery/docs

cloud.google.com/datastore/docs

docs.snowflake.net/manuals/index.html

www.tarantool.io/en/doc

weaviate.io/developers/weaviate

Developer

Google

Snowflake Computing Inc.

Weaviate B.V.

Initial release

2010

2008

2014

2008

2019

Current release

2.10.0, May 2022

1.19, May 2023

License

Commercial or Open Source

commercial

Open Source

BSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise

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

C and C++

Server operating systems

hosted

BSD
Linux
macOS

Data scheme

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

Flexible data schema: relational definition for tables with ability to store json-like documents in columns

yes, maps to GraphQL interface

Typing

predefined data types such as float or date

yes

yes, details here

yes

string, double, decimal, uuid, integer, blob, boolean, datetime

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.

yes

Secondary indexes

yes

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

SQL

Support of SQL

yes

SQL-like query language (GQL)

yes

Full-featured ANSI SQL support

GraphQL is used as query language

APIs and other access methods

RESTful HTTP/JSON API

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

CLI Client
JDBC
ODBC

Open binary protocol

GraphQL query language
RESTful HTTP/JSON API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

JavaScript (Node.js)
Python

C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust

JavaScript / TypeScript
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

using Google App Engine

user defined functions

Lua, C and SQL stored procedures

Triggers

Callbacks using the Google Apps Engine

similar concept for controling cloud resources

yes, before/after data modification events, on replication events, client session events

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes

Sharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

yes

Asynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Casual consistency across sharding partitions
Eventual consistency within replicaset partition

when using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition

when using Raft

Eventual Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

ACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions

Concurrency

Support for concurrent manipulation of data

yes

yes, cooperative multitasking

yes

Durability

Support for making data persistent

yes

yes, write ahead logging

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes, full featured in-memory storage engine with persistence

yes

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Users with fine-grained authorization concept, user roles and pluggable authentication

Access Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

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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