DBMS > EventStoreDB vs. Google Cloud Bigtable vs. Google Cloud Firestore vs. HBase vs. SwayDB

System Properties Comparison EventStoreDB vs. Google Cloud Bigtable vs. Google Cloud Firestore vs. HBase vs. SwayDB

Editorial information provided by DB-Engines

Name

Description

Industrial-strength, open-source database solution built from the ground up for event sourcing.

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.

Wide-column store based on Apache Hadoop and on concepts of BigTable

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

Primary database model

Event Store

Key-value store
Wide column store

Document store

Wide column store

Key-value store

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

Score	1.10
Rank	#179	Overall
	#1	Event Stores

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

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	0.00
Rank	#382	Overall
	#59	Key-value stores

Website

www.eventstore.com

cloud.google.com/bigtable

firebase.google.com/products/firestore

hbase.apache.org

swaydb.simer.au

Technical documentation

developers.eventstore.com

cloud.google.com/bigtable/docs

firebase.google.com/docs/firestore

hbase.apache.org/book.html

Developer

Event Store Limited

Google

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Simer Plaha

Initial release

2012

2015

2017

2008

2018

Current release

21.2, February 2021

2.3.4, January 2021

License

Commercial or Open Source

Open Source

commercial

Open Source

Apache version 2

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

Java

Scala

Server operating systems

Linux
Windows

hosted

Linux
Unix
Windows

using Cygwin

Data scheme

schema-free

schema-free, schema definition possible

schema-free

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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

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
RESTful HTTP API
Thrift

Supported programming languages

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

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

C
C#
C++
Groovy
Java
PHP
Python
Scala

Java
Kotlin
Scala

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

yes

Coprocessors in Java

Triggers

yes, with Cloud Functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

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

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Using Cloud Dataflow

yes

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

Immediate Consistency or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomic single-row operations

yes

Single row ACID (across millions of columns)

Atomic execution of operations

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

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.

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

More information provided by the system vendor

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More resources

DB-Engines blog posts

Cloud-based DBMS's popularity grows at high rates
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