DBMS > GeoMesa vs. Heroic vs. Microsoft Azure SQL Database vs. Microsoft Azure Synapse Analytics vs. TDengine

System Properties Comparison GeoMesa vs. Heroic vs. Microsoft Azure SQL Database vs. Microsoft Azure Synapse Analytics vs. TDengine

Editorial information provided by DB-Engines

Name

Description

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

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

Database as a Service offering with high compatibility to Microsoft SQL Server

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

Time Series DBMS and big data platform

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Relational DBMS

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

Score	0.86
Rank	#205	Overall
	#4	Spatial DBMS

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	76.78
Rank	#16	Overall
	#11	Relational DBMS

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Score	2.68
Rank	#106	Overall
	#9	Time Series DBMS

Website

www.geomesa.org

github.com/spotify/heroic

azure.microsoft.com/en-us/products/azure-sql/database

azure.microsoft.com/services/synapse-analytics

github.com/taosdata/TDengine
tdengine.com

Technical documentation

www.geomesa.org/documentation/stable/user/index.html

spotify.github.io/heroic

docs.microsoft.com/en-us/azure/azure-sql

docs.microsoft.com/azure/synapse-analytics

docs.tdengine.com

Developer

CCRi and others

Spotify

Microsoft

TDEngine, previously Taos Data

Initial release

2014

2010

2016

2019

Current release

5.0.0, May 2024

V12

3.0, August 2022

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

Apache 2.0

commercial

Open Source

AGPL V3, also commercial editions 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

Scala

Java

C++

Server operating systems

hosted

Linux
Windows

Data scheme

yes

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

via Elasticsearch

yes

SQL

Support of SQL

yes

Standard SQL with extensions for time-series applications

APIs and other access methods

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

ADO.NET
JDBC
ODBC

JDBC
RESTful HTTP API

Supported programming languages

.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby

C#
Java
PHP

C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Rust

Server-side scripts

Stored procedures

Transact SQL

Triggers

yes

yes, via alarm monitoring

Partitioning methods

Methods for storing different data on different nodes

depending on storage layer

Sharding

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

depending on storage layer

yes

yes, with always 3 replicas available

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

depending on storage layer

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

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.

depending on storage layer

User concepts

Access control

yes

depending on the DBMS used for storage

fine grained access rights according to SQL-standard

yes

More information provided by the system vendor

Specific characteristics

TDengine™ is a next generation data historian purpose-built for Industry 4.0 and...
» more

Competitive advantages

High Performance at any Scale: TDengine is purpose-built for handling massive industrial...
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Typical application scenarios

TDengine is designed for Industrial IoT scenarios, including: Manufacturing Connected...
» more

Market metrics

TDengine has garnered over 22,500 stars on GitHub and is used in over 50 countries...
» more

Licensing and pricing models

TDengine OSS is an open source, cloud native time series database. It includes built-in...
» more

News

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6 June 2024

Comprehensive Comparison Between TDengine and TimescaleDB
5 June 2024

Mastering Memory Leak Detection in TDengine
31 May 2024

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22 May 2024

Solving Long Query Performance Bottlenecks
22 May 2024

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

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More resources

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7 September 2016, InfoWorld

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