Amazon Redshift vs PostGIS: What are the differences?
Developers describe Amazon Redshift as "Fast, fully managed, petabyte-scale data warehouse service". Redshift makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools. It is optimized for datasets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions. On the other hand, PostGIS is detailed as "Open source spatial database". PostGIS is a spatial database extender for PostgreSQL object-relational database. It adds support for geographic objects allowing location queries to be run in SQL.
Amazon Redshift and PostGIS are primarily classified as "Big Data as a Service" and "Database" tools respectively.
Some of the features offered by Amazon Redshift are:
- Optimized for Data Warehousing- It uses columnar storage, data compression, and zone maps to reduce the amount of IO needed to perform queries. Redshift has a massively parallel processing (MPP) architecture, parallelizing and distributing SQL operations to take advantage of all available resources.
- Scalable- With a few clicks of the AWS Management Console or a simple API call, you can easily scale the number of nodes in your data warehouse up or down as your performance or capacity needs change.
- No Up-Front Costs- You pay only for the resources you provision. You can choose On-Demand pricing with no up-front costs or long-term commitments, or obtain significantly discounted rates with Reserved Instance pricing.
On the other hand, PostGIS provides the following key features:
- Processing and analytic functions for both vector and raster data for splicing, dicing, morphing, reclassifying, and collecting/unioning with the power of SQL
- raster map algebra for fine-grained raster processing
- Spatial reprojection SQL callable functions for both vector and raster data
"Data Warehousing" is the primary reason why developers consider Amazon Redshift over the competitors, whereas "De facto GIS in SQL" was stated as the key factor in picking PostGIS.
PostGIS is an open source tool with 645 GitHub stars and 246 GitHub forks. Here's a link to PostGIS's open source repository on GitHub.
According to the StackShare community, Amazon Redshift has a broader approval, being mentioned in 269 company stacks & 67 developers stacks; compared to PostGIS, which is listed in 53 company stacks and 15 developer stacks.
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Looker , Stitch , Amazon Redshift , dbt
We recently moved our Data Analytics and Business Intelligence tooling to Looker . It's already helping us create a solid process for reusable SQL-based data modeling, with consistent definitions across the entire organizations. Looker allows us to collaboratively build these version-controlled models and push the limits of what we've traditionally been able to accomplish with analytics with a lean team.
For Data Engineering, we're in the process of moving from maintaining our own ETL pipelines on AWS to a managed ELT system on Stitch. We're also evaluating the command line tool, dbt to manage data transformations. Our hope is that Stitch + dbt will streamline the ELT bit, allowing us to focus our energies on analyzing data, rather than managing it.
Aggressive archiving of historical data to keep the production database as small as possible. Using our in-house soon-to-be-open-sourced ETL library, SharpShifter.
PostGIS makes it easy (and fast) to do geographic queries, such as nearest-neighbor and bounding box queries.
Backend for weather forecast data that Geoserver queries to build updated weather maps