DBMS > Datomic vs. Microsoft Azure Synapse Analytics vs. Transwarp Hippo vs. YDB

System Properties Comparison Datomic vs. Microsoft Azure Synapse Analytics vs. Transwarp Hippo vs. YDB

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Editorial information provided by DB-Engines

Name

Description

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

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

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

A distributed fault-tolerant database service, with high availability, scalability, immediate consistency and ACID transactions and providing an Amazon DynamoDB compatible API

Primary database model

Relational DBMS

Vector DBMS

Document 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	0.05
Rank	#386	Overall
	#14	Vector DBMS

Score	0.33
Rank	#287	Overall
	#43	Document stores
	#132	Relational DBMS

Website

www.datomic.com

azure.microsoft.com/services/synapse-analytics

www.transwarp.cn/en/subproduct/hippo

github.com/ydb-platform/ydb
ydb.tech

Technical documentation

docs.datomic.com

docs.microsoft.com/azure/synapse-analytics

ydb.tech/en/docs

Developer

Cognitect

Microsoft

Yandex

Initial release

2012

2016

2023

2019

Current release

1.0.7075, December 2023

1.0, May 2023

License

Commercial or Open Source

commercial

limited edition free

commercial

Open Source

Apache 2.0; 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, Clojure

C++

Server operating systems

All OS with a Java VM

hosted

Linux
macOS

Linux

Data scheme

yes

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

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

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

SQL-like query language (YQL)

APIs and other access methods

RESTful HTTP API

ADO.NET
JDBC
ODBC

RESTful HTTP API

RESTful HTTP API (DynamoDB compatible)

Supported programming languages

Clojure
Java

C#
Java
PHP

C++
Java
Python

Go
Java
JavaScript (Node.js)
PHP
Python

Server-side scripts

Stored procedures

yes

Transaction Functions

Transact SQL

Triggers

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

yes

Active-passive shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

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

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

yes

User concepts

Access control

yes

Role based access control and fine grained access rights

Access rights defined for Yandex Cloud users

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