DBMS > Alibaba Cloud Table Store vs. Amazon DynamoDB vs. Databricks vs. EsgynDB vs. JaguarDB

System Properties Comparison Alibaba Cloud Table Store vs. Amazon DynamoDB vs. Databricks vs. EsgynDB vs. JaguarDB

Editorial information provided by DB-Engines

Name

Description

A fully managed Wide Column Store for large quantities of semi-structured data with real-time access

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.

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

Performant, highly scalable DBMS for AI and IoT applications

Primary database model

Wide column store

Document store
Key-value store

Document store
Relational DBMS

Relational DBMS

Key-value store
Vector DBMS

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

Score	0.31
Rank	#297	Overall
	#11	Wide column stores

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	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	0.06
Rank	#381	Overall
	#59	Key-value stores
	#13	Vector DBMS

Website

www.alibabacloud.com/product/table-store

aws.amazon.com/dynamodb

www.databricks.com

www.esgyn.cn

www.jaguardb.com

Technical documentation

www.alibabacloud.com/help/en/tablestore

docs.aws.amazon.com/dynamodb

docs.databricks.com

www.jaguardb.com/support.html

Developer

Alibaba

Amazon

Databricks

Esgyn

DataJaguar, Inc.

Initial release

2016

2012

2013

2015

Current release

3.3 July 2023

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Open Source

GPL V3.0

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

C++

the server part. Clients available in other languages

Server operating systems

hosted

Linux

Data scheme

schema-free

Flexible Schema (defined schema, partial schema, 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

with Databricks SQL

yes

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

APIs and other access methods

HTTP API

RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

JDBC
ODBC

Supported programming languages

Java
Python

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

Python
R
Scala

All languages supporting JDBC/ODBC/ADO.Net

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions and aggregates

Java Stored Procedures

Triggers

yes

by integration with AWS Lambda

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes

Multi-source replication between multi datacenters

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic single-row operations

ACID

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

ACID

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.

User concepts

Access control

Access rights based on subaccounts and tokens

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

rights management via user accounts

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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