DBMS > Amazon Redshift vs. EventStoreDB vs. Google Cloud Bigtable vs. InfluxDB vs. Oracle Coherence

System Properties Comparison Amazon Redshift vs. EventStoreDB vs. Google Cloud Bigtable vs. InfluxDB vs. Oracle Coherence

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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.

DBMS for storing time series, events and metrics

Oracles in-memory data grid solution

Primary database model

Relational DBMS

Event Store

Key-value store
Wide column store

Time Series DBMS

Key-value store

Secondary database models

Spatial DBMS

with GEO package

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	1.10
Rank	#179	Overall
	#1	Event Stores

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

Score	25.83
Rank	#28	Overall
	#1	Time Series DBMS

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

Website

aws.amazon.com/redshift

www.eventstore.com

cloud.google.com/bigtable

www.influxdata.com/products/influxdb-overview

www.oracle.com/java/coherence

Technical documentation

docs.aws.amazon.com/redshift

developers.eventstore.com

cloud.google.com/bigtable/docs

docs.influxdata.com/influxdb

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

Developer

Amazon (based on PostgreSQL)

Event Store Limited

Google

Oracle

Initial release

2012

2015

2013

2007

Current release

21.2, February 2021

2.7.6, April 2024

14.1, August 2023

License

Commercial or Open Source

commercial

Open Source

commercial

Open Source

MIT-License; commercial enterprise version available

commercial

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

Server operating systems

hosted

Linux
Windows

hosted

Linux
OS X

through Homebrew

All OS with a Java VM

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

Numeric data and Strings

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

restricted

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language

APIs and other access methods

JDBC
ODBC

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

HTTP API
JSON over UDP

JCache
JPA
RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

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

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

.Net
C++
Java

Server-side scripts

Stored procedures

user defined functions

in Python

Triggers

yes

Live Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

in enterprise version only

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

selectable replication factor

in enterprise version only

yes, with selectable consistency level

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

Atomic single-row operations

configurable

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

In-memory capabilities

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

yes

Depending on used storage engine

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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

simple rights management via user accounts

authentification to access the cache via certificates or http basic authentication

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