DBMS > Amazon DynamoDB vs. IBM Db2 Event Store vs. Microsoft Azure Table Storage vs. Splice Machine

System Properties Comparison Amazon DynamoDB vs. IBM Db2 Event Store vs. Microsoft Azure Table Storage vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

Distributed Event Store optimized for Internet of Things use cases

A Wide Column Store for rapid development using massive semi-structured datasets

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

Primary database model

Document store
Key-value store

Event Store
Time Series DBMS

Wide column store

Relational DBMS

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

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

aws.amazon.com/dynamodb

www.ibm.com/products/db2-event-store

azure.microsoft.com/en-us/services/storage/tables

splicemachine.com

Technical documentation

docs.aws.amazon.com/dynamodb

www.ibm.com/docs/en/db2-event-store

splicemachine.com/how-it-works

Developer

Amazon

IBM

Microsoft

Splice Machine

Initial release

2012

2017

2012

2014

Current release

2.0

3.1, March 2021

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

free developer edition available

commercial

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

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Implementation language

C and C++

Java

Server operating systems

hosted

Linux

Linux, macOS, Windows for the developer addition

hosted

Linux
OS X
Solaris
Windows

Data scheme

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

yes

through the embedded Spark runtime

yes

APIs and other access methods

RESTful HTTP API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

yes

Java

Triggers

yes

by integration with AWS Lambda

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Active-active shard replication

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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)

fine grained access rights according to SQL-standard

Access rights based on private key authentication or shared access signatures

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

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