DBMS > HEAVY.AI vs. KairosDB vs. Microsoft Azure Synapse Analytics vs. Warp 10

System Properties Comparison HEAVY.AI vs. KairosDB vs. Microsoft Azure Synapse Analytics vs. Warp 10

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Editorial information provided by DB-Engines

Name

Description

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Distributed Time Series DBMS based on Cassandra or H2

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

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Secondary database models

Spatial DBMS

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

Score	2.10
Rank	#126	Overall
	#61	Relational DBMS

Score	0.71
Rank	#230	Overall
	#20	Time Series DBMS

Score	22.71
Rank	#30	Overall
	#18	Relational DBMS

Score	0.08
Rank	#358	Overall
	#35	Time Series DBMS

Website

github.com/heavyai/heavydb
www.heavy.ai

github.com/kairosdb/kairosdb

azure.microsoft.com/services/synapse-analytics

www.warp10.io

Technical documentation

docs.heavy.ai

kairosdb.github.io

docs.microsoft.com/azure/synapse-analytics

www.warp10.io/content/02_Getting_started

Developer

HEAVY.AI, Inc.

Microsoft

SenX

Initial release

2016

2013

2016

2015

Current release

5.10, January 2022

1.2.2, November 2018

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache 2.0

commercial

Open Source

Apache License 2.0

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++ and CUDA

Java

C++

Java

Server operating systems

Linux

Linux
OS X
Windows

hosted

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

JDBC
ODBC
Thrift
Vega

Graphite protocol
HTTP REST
Telnet API

ADO.NET
JDBC
ODBC

HTTP API
Jupyter
WebSocket

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

Java
JavaScript

Node.js
PHP
Python

C#
Java
PHP

Server-side scripts

Stored procedures

Transact SQL

yes

WarpScript

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

based on Cassandra

Sharding, horizontal partitioning

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication factor

based on Cassandra

yes

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

based on HBase

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

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

simple password-based access control

yes

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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