DBMS > Amazon Redshift vs. Heroic vs. Microsoft Azure Synapse Analytics vs. Warp 10

System Properties Comparison Amazon Redshift vs. Heroic vs. Microsoft Azure Synapse Analytics vs. Warp 10

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

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

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

Score	19.03
Rank	#34	Overall
	#21	Relational DBMS

Score	0.57
Rank	#250	Overall
	#21	Time Series DBMS

Score	22.71
Rank	#30	Overall
	#18	Relational DBMS

Score	0.08
Rank	#358	Overall
	#35	Time Series DBMS

Website

aws.amazon.com/redshift

github.com/spotify/heroic

azure.microsoft.com/services/synapse-analytics

www.warp10.io

Technical documentation

docs.aws.amazon.com/redshift

spotify.github.io/heroic

docs.microsoft.com/azure/synapse-analytics

www.warp10.io/content/02_Getting_started

Developer

Amazon (based on PostgreSQL)

Spotify

Microsoft

SenX

Initial release

2012

2014

2016

2015

License

Commercial or Open Source

commercial

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

Java

C++

Java

Server operating systems

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

restricted

yes

via Elasticsearch

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

APIs and other access methods

JDBC
ODBC

HQL (Heroic Query Language, a JSON-based language)
HTTP API

ADO.NET
JDBC
ODBC

HTTP API
Jupyter
WebSocket

Supported programming languages

All languages supporting JDBC/ODBC

C#
Java
PHP

Server-side scripts

Stored procedures

user defined functions

in Python

Transact SQL

yes

WarpScript

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

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
Immediate Consistency

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

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

yes

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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