DBMS > Google BigQuery vs. Realm vs. Sequoiadb vs. Spark SQL

System Properties Comparison Google BigQuery vs. Realm vs. Sequoiadb vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

NewSQL database with distributed OLTP and SQL

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

Document store

Document store
Relational DBMS

Relational DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	7.41
Rank	#52	Overall
	#8	Document stores

Score	0.50
Rank	#258	Overall
	#41	Document stores
	#120	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

cloud.google.com/bigquery

realm.io

www.sequoiadb.com

spark.apache.org/sql

Technical documentation

cloud.google.com/bigquery/docs

realm.io/docs

www.sequoiadb.com/en/index.php?m=Files&a=index

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Google

Realm, acquired by MongoDB in May 2019

Sequoiadb Ltd.

Apache Software Foundation

Initial release

2010

2014

2013

2014

Current release

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Server: AGPL; Client: Apache V2

Open Source

Apache 2.0

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

C++

Scala

Server operating systems

hosted

Android
Backend: server-less
iOS
Windows

Linux

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP/JSON API

proprietary protocol using JSON

JDBC
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
Java

with Android only
Objective-C
React Native
Swift

.Net
C++
Java
PHP
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in JavaScript

runs within the applications so server-side scripts are unnecessary

JavaScript

Triggers

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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

ACID

Document is locked during a transaction

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

In-Memory realm

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

yes

simple password-based access control

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