DBMS > Databricks vs. Dragonfly vs. Fauna vs. Google BigQuery vs. H2

System Properties Comparison Databricks vs. Dragonfly vs. Fauna vs. Google BigQuery vs. H2

Editorial information provided by DB-Engines

Name

Description

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

A drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

Fauna provides a web-native interface, with support for GraphQL and custom business logic that integrates seamlessly with the rest of the serverless ecosystem. The underlying globally distributed storage and compute platform is fast, consistent, and reliable, with a modern security infrastructure.

Large scale data warehouse service with append-only tables

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Primary database model

Document store
Relational DBMS

Key-value store

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	81.08
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	0.49
Rank	#261	Overall
	#38	Key-value stores

Score	1.55
Rank	#151	Overall
	#26	Document stores
	#14	Graph DBMS
	#71	Relational DBMS
	#13	Time Series DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	8.33
Rank	#46	Overall
	#30	Relational DBMS

Website

www.databricks.com

github.com/dragonflydb/dragonfly
www.dragonflydb.io

fauna.com

cloud.google.com/bigquery

www.h2database.com

Technical documentation

docs.databricks.com

www.dragonflydb.io/docs

docs.fauna.com

cloud.google.com/bigquery/docs

www.h2database.com/html/main.html

Developer

Databricks

DragonflyDB team and community contributors

Fauna, Inc.

Google

Thomas Mueller

Initial release

2013

2023

2014

2010

2005

Current release

1.0, March 2023

2.2.220, July 2023

License

Commercial or Open Source

commercial

Open Source

BSL 1.1

commercial

Open Source

dual-licence (Mozilla public license, Eclipse public license)

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++

Scala

Java

Server operating systems

hosted

Linux

hosted

All OS with a Java VM

Data scheme

Flexible Schema (defined schema, partial schema, schema free)

scheme-free

schema-free

yes

Typing

predefined data types such as float or date

strings, hashes, lists, sets, sorted sets, bit arrays

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.

yes

Secondary indexes

yes

SQL

Support of SQL

with Databricks SQL

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

Proprietary protocol

RESP - REdis Serialization Protocol

RESTful HTTP API

RESTful HTTP/JSON API

JDBC
ODBC

Supported programming languages

Python
R
Scala

C
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java

Server-side scripts

Stored procedures

user defined functions and aggregates

Lua

user defined functions

in JavaScript

Java Stored Procedures and User-Defined Functions

Triggers

publish/subscribe channels provide some trigger functionality

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

consistent hashing

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

Multi-source replication

With clustering: 2 database servers on different computers operate on identical copies of a database

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic execution of command blocks and scripts

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, strict serializability by the server

yes

yes, multi-version concurrency control (MVCC)

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

Password-based authentication

Identity management, authentication, and access control

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
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