DBMS > Amazon SimpleDB vs. BigObject vs. EJDB vs. Splice Machine

System Properties Comparison Amazon SimpleDB vs. BigObject vs. EJDB vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

Hosted simple database service by Amazon, with the data stored in the Amazon Cloud.

There is an unrelated product called SimpleDB developed by Edward Sciore

Analytic DBMS for real-time computations and queries

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Key-value store

Relational DBMS

a hierachical model (tree) can be imposed

Document store

Relational DBMS

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

Score	1.85
Rank	#138	Overall
	#24	Key-value stores

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

aws.amazon.com/simpledb

bigobject.io

github.com/Softmotions/ejdb

splicemachine.com

Technical documentation

docs.aws.amazon.com/simpledb

docs.bigobject.io

github.com/Softmotions/ejdb/blob/master/README.md

splicemachine.com/how-it-works

Developer

Amazon

BigObject, Inc.

Softmotions

Splice Machine

Initial release

2007

2015

2012

2014

Current release

3.1, March 2021

License

Commercial or Open Source

commercial

free community edition available

Open Source

GPLv2

Open Source

AGPL 3.0, commercial license 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

Java

Server operating systems

hosted

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

server-less

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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

yes

All columns are indexed automatically

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

RESTful HTTP API

fluentd
ODBC
RESTful HTTP API

in-process shared library

JDBC
Native Spark Datasource
ODBC

Supported programming languages

.Net
C
C++
Erlang
Java
PHP
Python
Ruby
Scala

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

Lua

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding must be implemented in the application

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

none

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

Concurrent data updates can be detected by the application

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

Read/Write Locking

yes, multi-version concurrency control (MVCC)

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 and roles can be defined via the AWS Identity and Access Management (IAM)

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

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More resources

DB-Engines blog posts

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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Recent citations in the news

New SimpleDB Goodies: Enhanced Select, Larger Result Sets, Mandatory HTTPS | Amazon Web Services
20 May 2009, AWS Blog

Amazon DynamoDB Serves Trillions Of Requests Per Month While Counterpart SimpleDB Is No Longer A Listed ...
12 November 2013, TechCrunch

Hands-on Tutorial for Getting Started with Amazon SimpleDB
28 May 2010, Packt Hub

Farewell EC2-Classic, it's been swell
1 September 2023, All Things Distributed

Amazon SimpleDB Management in Eclipse | AWS News Blog
22 July 2009, AWS Blog

provided by Google News

Machine learning data pipeline outfit Splice Machine files for insolvency
26 August 2021, The Register

Splice Machine Launches the Splice Machine Feature Store to Simplify Feature Engineering and Democratize Machine ...
19 January 2021, PR Newswire

Splice Machine Launches Feature Store to Simplify Feature Engineering
19 January 2021, Datanami

Distributed SQL System Review: Snowflake vs Splice Machine
18 September 2019, Towards Data Science

Splice Machine splices into AWS
8 February 2017, SDTimes.com

provided by Google News

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