DBMS > Google Cloud Bigtable vs. Google Cloud Firestore vs. Oracle Berkeley DB vs. Oracle Coherence vs. Sadas Engine

System Properties Comparison Google Cloud Bigtable vs. Google Cloud Firestore vs. Oracle Berkeley DB vs. Oracle Coherence vs. Sadas Engine

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.

Widely used in-process key-value store

Oracles in-memory data grid solution

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Primary database model

Key-value store
Wide column store

Document store

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Key-value store

Relational DBMS

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

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	1.92
Rank	#130	Overall
	#22	Key-value stores

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Website

cloud.google.com/bigtable

firebase.google.com/products/firestore

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

www.oracle.com/java/coherence

www.sadasengine.com

Technical documentation

cloud.google.com/bigtable/docs

firebase.google.com/docs/firestore

docs.oracle.com/cd/E17076_05/html/index.html

docs.oracle.com/en/middleware/standalone/coherence

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

Google

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Oracle

SADAS s.r.l.

Initial release

2015

2017

1994

2007

2006

Current release

18.1.40, May 2020

14.1, August 2023

8.0

License

Commercial or Open Source

commercial

Open Source

commercial license available

commercial

free trial version available

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, Java, C++ (depending on the Berkeley DB edition)

Java

C++

Server operating systems

hosted

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

All OS with a Java VM

AIX
Linux
Windows

Data scheme

schema-free

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

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

yes

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

JCache
JPA
RESTful HTTP API

JDBC
ODBC
Proprietary protocol

Supported programming languages

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

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

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

.Net
C++
Java

.Net
C
C#
C++
Groovy
Java
PHP
Python

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

Triggers

yes, with Cloud Functions

yes

only for the SQL API

yes

Live Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

horizontal partitioning

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

Source-replica replication

yes, with selectable consistency level

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Using Cloud Dataflow

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

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic single-row operations

yes

ACID

configurable

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

yes

In-memory capabilities

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

yes

managed by 'Learn by Usage'

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.

authentification to access the cache via certificates or http basic authentication

Access rights for users, groups and roles according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

Cloud-based DBMS's popularity grows at high rates
12 December 2019, Paul Andlinger

show all

Recent citations in the news

Google's AI-First Strategy Brings Vector Support To Cloud Databases
1 March 2024, Forbes

Google announces Axion, its first Arm-based CPU for data centers
9 April 2024, Yahoo Movies Canada

Google Introduces Autoscaling for Cloud Bigtable for Optimizing Costs
31 January 2022, InfoQ.com

Review: Google Bigtable scales with ease
7 September 2016, InfoWorld

Google scales up Cloud Bigtable NoSQL database
27 January 2022, TechTarget

provided by Google News

Realtime vs Cloud Firestore: Which Firebase Database to go?
8 March 2024, Appinventiv

Google's AI-First Strategy Brings Vector Support To Cloud Databases
1 March 2024, Forbes

Google's Cloud Firestore is now generally available
31 January 2019, ZDNet

Google launches Cloud Firestore, a new document database for app developers
3 October 2017, TechCrunch

Firestore and Python | NoSQL on Google Cloud
7 August 2020, Towards Data Science

provided by Google News

ACM recognizes far-reaching technical achievements with special awards
26 May 2021, EurekAlert

Database Trends Report: SQL Beats NoSQL, MySQL Most Popular -- ADTmag
5 March 2019, ADT Magazine

The importance of bitcoin nodes and how to start one
9 May 2014, The Merkle News

A Quick Look at Open Source Databases for Mobile App Development
29 April 2018, Open Source For You

Motorola A780 Linux based smartphone to have mobile database
14 September 2004, Geekzone

provided by Google News

Create custom Event Compression Policies in Enterprise Manager to reduce event noise
21 September 2023, Oracle

Microservices with Major New Coherence and Helidon Releases
9 August 2022, Oracle

Nike achieves scalability and performance with Oracle Coherence & Exadata
20 December 2012, Oracle

New solutions for Oracle WebLogic on Azure Virtual Machines
1 April 2021, Microsoft

GridGain: Product Overview and Analysis
5 June 2019, eWeek

provided by Google News

Share this page