DBMS > Google Cloud Bigtable vs. Google Cloud Firestore vs. MarkLogic vs. Spark SQL

System Properties Comparison Google Cloud Bigtable vs. Google Cloud Firestore vs. MarkLogic vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Cloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.

Operational and transactional Enterprise NoSQL database

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

Primary database model

Key-value store
Wide column store

Document store

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Relational DBMS

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

Score	4.84
Rank	#80	Overall
	#13	Key-value stores
	#6	Wide column stores

Score	10.03
Rank	#49	Overall
	#8	Document stores

Score	9.62
Rank	#52	Overall
	#10	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#4	Search engines

Score	23.72
Rank	#35	Overall
	#21	Relational DBMS

Website

cloud.google.com/bigtable

firebase.google.com/products/firestore

www.marklogic.com

spark.apache.org/sql

Technical documentation

cloud.google.com/bigtable/docs

firebase.google.com/docs/firestore

docs.marklogic.com

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

Developer

Google

MarkLogic Corp.

Apache Software Foundation

Initial release

2015

2017

2001

2014

Current release

9.0, 2017

3.3.0, June 2022

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Scala

Server operating systems

hosted

Linux
OS X
Windows

Data scheme

schema-free

Schema can be enforced

yes

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

yes

SQL92

SQL-like DML and DDL statements

APIs and other access methods

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

JDBC
ODBC

Supported programming languages

C#
C++
Go
Java
JavaScript (Node.js)
Python

Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python

C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

yes

via XQuery or JavaScript

Triggers

yes, with Cloud Functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Internal replication in Colossus, and regional replication between two clusters in different zones

Multi-source replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Using Cloud Dataflow

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomic single-row operations

yes

ACID

can act as a resource manager in an XA/JTA 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, with Range Indexes

User concepts

Access control

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

Role-based access control at the document and subdocument levels

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