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What are the top 5 popular Databases and what is their Spatial support?

What are the top 5 popular Databases and what is their Spatial support? 🔝🛢️🌎

I am writing a lot about using databases and cloud data warehouses☁️ with Spatial data and analysis.

I am doing so because i love data, databases and especially the Spatial world.

I see it as a necessity that will be used in almost any place, sooner or later.

In this article, I made a lists of the most popular: Relational, No-SQL databases and cloud data warehouses (with the aid of OpenAI’s answers).

The Purpose of this post is to check what are the most popular databases whether they are SQL/no-SQL. commercial/open source, and checking if they have support for spatial and its level.🌎

So whether you have one of them or you are planning to use one, you will see if it has spatial support, so you can implement spatial analysis and data with simple SQL language in your current database or over the cloud as if you have a GIS system.

If you will look at the results you will be surprised.🎁

Top 5 Relational Databases:

  1. MySQL: One of the most popular databases. It is known for its reliability, ease of use, and wide adoption across various industries. Open-source. MySQL offers Spatial extensions that provide support for spatial data types, as well as a wide set of spatial functions

  2. PostgreSQL: PostgreSQL is a powerful popular database known for its extensibility, standards compliance, and advanced features. Open-source. PostgreSQL has the richest Spatial support through its PostGIS extension, making it a popular choice for GIS applications and spatial analysis

  3. Microsoft SQL Server: SQL Server is a popular relational database. It is widely used in enterprise environments, easy to use and to maintain. SQL Server includes support for Spatial data types and provides a wide set of spatial functions for working with spatial data. Commercial. Spatial with no extra charge

  4. Oracle: Oracle is a popular relational database. It is widely used in enterprise environments, for mission-critical applications and large-scale data management. Oracle Spatial support is an option for Oracle Database. It supports Spatial data types and offers a wide range of basic and advanced spatial functions. Commercial. Spatial with no extra charge

  5. SQLite: SQLite is a lightweight server-less database. Popular for embedded systems, mobile applications, and simple database applications. open-source. It has wide Spatial support with the SpatiaLite extension. Supports spatial data types and has a large range of spatial functions.

Top 5 No SQL Databases:

  1. MongoDB: Very popular No-SQL database, document-oriented. Known for its flexibility and scalability, for enterprise and small scale. It has relatively small Spatial support, but it should be enough for most use cases;  extensions or plugins may be used for more advanced spatial needs. Open-source.

  2. Cassandra: A popular Distributed No-SQL database. It is designed for high availability and scalability for large amounts of real-time, IoT, analytics data. Open-source It has a relatively small Spatial support, but it should be enough for most simple use cases. Open-source

  3. Couchbase: A popular distributed No-SQL database. It is known for its high performance, scalability, and flexible data model. It has a relatively small Spatial support, but it should be enough for most simple use cases. Open-source

  4. DynamoDB: Fully managed popular No-SQL database service by AWS cloud. It has a relatively small Spatial support, but it should be enough for most simple use cases. Commercial

  5. Neo4j: A popular Graph database. Known for its native graph processing. It has a relatively small spatial support, but it should be enough for most simple use cases. Offers an Interesting Combination of a graph and Spatial data use cases. Open-source

Top 5 Cloud Data Warehouses:

  1. Amazon Redshift, Amazon Athena: Redshift is A fully managed data warehouse service in the cloud. It’s known for its scalability, performance. Mostly used for structured data. Athena is a data warehouse used for raw, unstructured data stored as files. It has medium Spatial support, but it should be enough for most use cases.

  2. Google BigQuery: Data warehouse for analytics and machine learning, known for its scalability and fast query performance. Wide Spatial capabilities and visualization

  3. Snowflake: Cloud-based data platform known for its flexibility and ability to separate compute and storage resources. medium Spatial support, but it should be enough for most use cases.

  4. Microsoft Azure Synapse Analytics: Data warehouse and data analytics capabilities service. Wide Spatial capabilities through SQL Server Spatial.

  5. IBM Db2 Warehouse: Cloud data warehouse service, known for its high performance and scalability. medium Spatial support, but it should be enough for most use cases.

** Disclaimer: This is not CONTEST!!!. All of them are good, and there are many more not listed. The purpose was to make a list of databases and spatial support, to check how wide its spread and easy to use for spatial technology.

💭Imagine you can implement it easily and fast in your organization utilizing your current infrastructure and resources. Whether it is a cloud platform, database, BI platform, or existing spatial/GIS system that you feel is non-utilized enough.
Implement spatial tech and be one step ahead!

 

 

👌 The bottom line: It will be a shame to miss the spatial dimension, it’s not complex to implement and the value can be enormous.

If you need help, we will be happy to assist…🙂

 

 

——————————————-
I’m Ran Tzkhori, a Geo-Spatial data and Spatial systems expert.

We help organizations find fast answers with the aid of spatial data and spatial SQL in their existing or new systems.

If you need advice, don’t hesitate to reach me.⬆️

 

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Hi There,
I am Ran, Mikoom’s Founder.
I will be happy to answer any question.
Just click on the button below to reach me.
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