DBMS > Datomic vs. Hypertable vs. Microsoft Azure Synapse Analytics vs. Snowflake

System Properties Comparison Datomic vs. Hypertable vs. Microsoft Azure Synapse Analytics vs. Snowflake

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Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

An open source BigTable implementation based on distributed file systems such as Hadoop

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

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

Primary database model

Relational DBMS

Wide column store

Relational DBMS

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

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

www.datomic.com

azure.microsoft.com/services/synapse-analytics

www.snowflake.com

Technical documentation

docs.datomic.com

docs.microsoft.com/azure/synapse-analytics

docs.snowflake.net/manuals/index.html

Developer

Cognitect

Hypertable Inc.

Microsoft

Snowflake Computing Inc.

Initial release

2012

2009

2016

2014

Current release

1.0.6735, June 2023

0.9.8.11, March 2016

License

Commercial or Open Source

commercial

limited edition free

Open Source

GNU version 3. Commercial license available

commercial

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, Clojure

C++

Server operating systems

All OS with a Java VM

Linux
OS X
Windows

an inofficial Windows port is available

hosted

Data scheme

yes

schema-free

yes

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

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

restricted

only exact value or prefix value scans

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP API

C++ API
Thrift

ADO.NET
JDBC
ODBC

CLI Client
JDBC
ODBC

Supported programming languages

Clojure
Java

C++
Java
Perl
PHP
Python
Ruby

C#
Java
PHP

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

Transaction Functions

Transact SQL

user defined functions

Triggers

By using transaction functions

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

Sharding, horizontal partitioning

yes

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

selectable replication factor on file system level

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

User concepts

Access control

yes

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

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