DBMS > Amazon DynamoDB vs. Snowflake vs. Splice Machine vs. Teradata Aster vs. WakandaDB

System Properties Comparison Amazon DynamoDB vs. Snowflake vs. Splice Machine vs. Teradata Aster vs. WakandaDB

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

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

Cloud-based data warehousing service for structured and semi-structured data

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

Platform for big data analytics on multistructured data sources and types

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Document store
Key-value store

Relational DBMS

Object oriented DBMS

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

Score	74.45
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Score	0.10
Rank	#356	Overall
	#16	Object oriented DBMS

Website

aws.amazon.com/dynamodb

www.snowflake.com

splicemachine.com

wakanda.github.io

Technical documentation

docs.aws.amazon.com/dynamodb

docs.snowflake.net/manuals/index.html

splicemachine.com/how-it-works

wakanda.github.io/doc

Developer

Amazon

Snowflake Computing Inc.

Splice Machine

Teradata

Wakanda SAS

Initial release

2012

2014

2005

2012

Current release

3.1, March 2021

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Open Source

AGPL 3.0, commercial license available

commercial

Open Source

AGPLv3, extended 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

C++, JavaScript

Server operating systems

hosted

Linux
OS X
Solaris
Windows

Linux

Linux
OS X
Windows

Data scheme

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

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

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP API

CLI Client
JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

ADO.NET
JDBC
ODBC
OLE DB

RESTful HTTP API

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

JavaScript (Node.js)
Python

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

C
C#
C++
Java
Python
R

JavaScript

Server-side scripts

Stored procedures

user defined functions

yes

Java

R packages

yes

Triggers

yes

by integration with AWS Lambda

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Shared Nothhing Auto-Sharding, Columnar Partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

none

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Yes, via Full Spark Integration

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

Users with fine-grained authorization concept, user roles and pluggable authentication

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

fine grained access rights according to SQL-standard

yes

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