DBMS > Amazon Aurora vs. EsgynDB vs. Google Cloud Bigtable vs. Splunk

System Properties Comparison Amazon Aurora vs. EsgynDB vs. Google Cloud Bigtable vs. Splunk

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Editorial information provided by DB-Engines

Name

Description

MySQL and PostgreSQL compatible cloud service by Amazon

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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

Analytics Platform for Big Data

Primary database model

Relational DBMS

Key-value store
Wide column store

Search engine

Secondary database models

Document store

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

Score	7.84
Rank	#44	Overall
	#28	Relational DBMS

Score	0.15
Rank	#325	Overall
	#144	Relational DBMS

Score	2.97
Rank	#92	Overall
	#15	Key-value stores
	#8	Wide column stores

Score	93.02
Rank	#13	Overall
	#2	Search engines

Website

aws.amazon.com/rds/aurora

www.esgyn.cn

cloud.google.com/bigtable

www.splunk.com

Technical documentation

docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/CHAP_Aurora.html

cloud.google.com/bigtable/docs

docs.splunk.com/Documentation/Splunk

Developer

Amazon

Esgyn

Google

Splunk Inc.

Initial release

2015

2003

License

Commercial or Open Source

commercial

Limited free edition and free developer edition 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

Server operating systems

hosted

Linux

hosted

Linux
OS X
Solaris
Windows

Data scheme

yes

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

Secondary indexes

yes

SQL

Support of SQL

yes

Splunk Search Processing Language for search commands

APIs and other access methods

ADO.NET
JDBC
ODBC

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

HTTP REST

Supported programming languages

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

All languages supporting JDBC/ODBC/ADO.Net

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

C#
Java
JavaScript
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

Java Stored Procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication between multi datacenters

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

Multi-source replication

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic single-row operations

A 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searching

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

fine grained access rights according to SQL-standard

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

Access rights for users and roles

More information provided by the system vendor

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

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More resources

DB-Engines blog posts

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

The popularity of cloud-based DBMSs has increased tenfold in four years
7 February 2017, Matthias Gelbmann

Amazon - the rising star in the DBMS market
3 August 2015, Matthias Gelbmann

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Enterprise Search Engines almost double their popularity in the last 12 months
2 July 2014, Paul Andlinger

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Recent citations in the news

Migrate SQL Server databases to Babelfish for Aurora PostgreSQL using change tracking with a linked server
24 September 2024, AWS Blog

Replace Amazon QLDB with Amazon Aurora PostgreSQL for audit use cases
18 July 2024, AWS Blog

Build generative AI applications with Amazon Aurora and Amazon Bedrock Knowledge Bases
2 February 2024, AWS Blog

Review your Amazon Aurora and Amazon RDS security configuration with Prowler’s new checks
6 August 2024, AWS Blog

Continuously replicate Amazon DynamoDB changes to Amazon Aurora PostgreSQL using AWS Lambda
14 May 2024, AWS Blog

provided by Google News

Google Cloud adds graph processing to Spanner, SQL support to Bigtable
1 August 2024, InfoWorld

Google introduces Bigtable SQL access and Spanner's new AI-ready features
1 August 2024, ZDNet

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

Google Cloud Adds GenAI, Core Enhancements Across Data Platform
1 August 2024, The New Stack

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

provided by Google News

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