DBMS > Amazon DynamoDB vs. Databricks vs. Snowflake vs. Splice Machine vs. WakandaDB

System Properties Comparison Amazon DynamoDB vs. Databricks vs. Snowflake vs. Splice Machine vs. WakandaDB

Editorial information provided by DB-Engines

Name

Description

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

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

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

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

Document store
Relational DBMS

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	81.08
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

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

Technical documentation

docs.aws.amazon.com/dynamodb

docs.databricks.com

docs.snowflake.net/manuals/index.html

splicemachine.com/how-it-works

wakanda.github.io/doc

Developer

Amazon

Databricks

Snowflake Computing Inc.

Splice Machine

Wakanda SAS

Initial release

2012

2013

2014

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

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

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Implementation language

Java

C++, JavaScript

Server operating systems

hosted

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

schema-free

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

yes

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

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

with Databricks SQL

yes

APIs and other access methods

RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

CLI Client
JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

RESTful HTTP API

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

Python
R
Scala

JavaScript (Node.js)
Python

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

JavaScript

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

yes

Java

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

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

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

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

yes

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
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