Data Engineering Tools for Geospatial Databy@dslan
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Data Engineering Tools for Geospatial Data

August 23rd 2022
4 min
by @dslan 331 reads
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There hasn’t been a lot written about data engineering ETL tools for geospatial data. In this blog post, we’ll focus on tools specifically designed for working with geospatial information.

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Data engineers are tasked with wrangling data and making it actionable. That means they spend a lot of time thinking about how to organize and store data so that anyone can find what they need quickly and easily. But there are also many challenges specific to working with geospatial data.

This type of data is usually stored as points or polygons on a grid, with each location having its own x and y coordinates. Geospatial data has many applications from mapping user locations to modeling weather systems.

It’s this kind of location-based information that makes the field of geospatial analytics so popular today and why data engineering can be such a challenging time handling this kind of dataset.

In general, there hasn’t been a lot written about data engineering ETL tools for geospatial data. In this blog post, we’ll focus on tools specifically designed for working with geospatial information.

Geospatial ETL Tools

ETL stands for Extract, Transform, and Load. It’s the process of taking raw data and getting it ready to be analyzed. There are many different types of data, and each type has its own best way to be processed. A geospatial data engineer might need to do any of the following tasks:

- Extract data from multiple sources, including databases, cloud storage, on-premises data, and third-party APIs.

- Convert raw data from one format to another. For example, transforming a database table from SQL to a KML file for Google Earth.

- Load satellite imagery into a geospatial database.

- Manipulate data to add spatial attributes or clean up errors. For example, standardizing addresses so they are in a usable format.

Here are the Geospatial ETL tools that can make this all happen

Apache Spark

Apache Spark is one of the most popular frameworks for geospatial data engineering. It’s an engine that can run in the cloud or on-premises. The Spark engine is designed to handle large amounts of data quickly while using less computing power. It uses parallel processing to break down big data sets into small tasks that can be processed simultaneously. Spark also has built-in libraries for working with geospatial data. This includes functions for searching for specific locations, calculating distances between locations, and integrating with external data sources.

Snowflake

Snowflake is an ETL tool that focuses primarily on loading data from disparate data sources into SaaS data warehouses. It is particularly adept at working with geospatial data. Snowflake can accept data from a number of different sources including cloud services such as Amazon Redshift. There are also connectors that allow you to load data from on-premises databases. A key feature of Snowflake is the ability to create custom functions. These functions can be used to transform the data as it’s being loaded into the system.

ArcGIS Pro

ArcGIS Pro is a powerful mapping tool designed for professional users. It can be used to process geospatial data and generate visualizations. While not a full-fledged ETL tool, ArcGIS Pro does have a few features that make it a good choice for data engineers. With Pro, you can load data from multiple sources including database tables, text files, and other common data formats. You can also integrate with external data sources, such as weather APIs. With the data loaded into Pro, you can perform a number of different types of analyses. These include finding trends, generating charts and graphs, and creating visualizations. ArcGIS Pro also allows you to create scatter plots, which can be useful for seeing how close one set of locations is to another set.

The Path to Geospatial Data Engineering

Data engineering is a career in which you work with data, which can be anything from structured and unstructured information like images or text. Data engineers generally have a strong background in computer science, mathematics, or statistics. They might also have a technical education such as a degree in computer programming or related fields.

In addition to the skills listed above, data engineers should also have the ability to think critically, be able to write clearly, and be comfortable with working in a team environment. But how do you gain these skills? Online programs are the new enabler for building these skillsets.

Summary

To summarize, geospatial data is a challenging type of data to work with. Ideally, you’d want to use a data engineering tool that is designed specifically for geospatial data. The best tools for geospatial data are the ones that are designed to handle large amounts of data and process it quickly. There are several tools that handle geospatial data well, including Apache Spark, Snowflake, and ArcGIS Pro.

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by Don @dslan.I still play 007 Goldeneye for Nintendo 64 and Bubble Bobble for NES
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