DBMS > Databricks vs. Google BigQuery vs. Oracle Rdb vs. SQL.JS vs. SwayDB

System Properties Comparison Databricks vs. Google BigQuery vs. Oracle Rdb vs. SQL.JS vs. SwayDB

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.

Large scale data warehouse service with append-only tables

Port of SQLite to JavaScript

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Document store
Relational DBMS

Relational DBMS

Key-value store

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	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1.14
Rank	#178	Overall
	#80	Relational DBMS

Score	0.63
Rank	#241	Overall
	#112	Relational DBMS

Score	0.04
Rank	#387	Overall
	#61	Key-value stores

Website

www.databricks.com

cloud.google.com/bigquery

www.oracle.com/database/technologies/related/rdb.html

sql.js.org

swaydb.simer.au

Technical documentation

docs.databricks.com

cloud.google.com/bigquery/docs

www.oracle.com/database/technologies/related/rdb-doc.html

sql.js.org/documentation/index.html

Developer

Databricks

Google

Oracle, originally developed by Digital Equipment Corporation (DEC)

Alon Zakai

enhancements implemented by others

Simer Plaha

Initial release

2013

2010

1984

2012

2018

Current release

7.4.1.1, 2021

License

Commercial or Open Source

commercial

Open Source

GNU Affero GPL V3.0

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

JavaScript

Scala

Server operating systems

hosted

HP Open VMS

server-less, requires a JavaScript environment (browser, Node.js)

Data scheme

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

yes

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

yes

schema-free

Typing

predefined data types such as float or date

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

SQL-92 is not fully supported

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

JavaScript API

Supported programming languages

Python
R
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

JavaScript

Java
Kotlin
Scala

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

in JavaScript

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

yes, on a single node

ACID

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

except by serializing a db to a file

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

Google Cloud Identity & Access Management (IAM)

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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