Amazon Redshift vs PostGIS

Overview

Amazon Redshift

Stacks1.5K

Followers1.4K

Votes108

PostGIS

Stacks381

Followers377

Votes30

GitHub Stars2.0K

Forks407

Amazon Redshift vs PostGIS: What are the differences?

Introduction

Amazon Redshift and PostGIS are both popular technologies used for data management and analysis, but they have key differences that make them unique in their own ways. In this article, we will explore the key differences between Amazon Redshift and PostGIS, providing specific details for each difference.

Scalability: Amazon Redshift is designed for large-scale data warehousing and analytics. It offers massive parallel processing capabilities, allowing it to handle petabytes of data and support thousands of concurrent users. On the other hand, PostGIS is an extension to the PostgreSQL database, which means it inherits PostgreSQL's scalability capabilities. While it can handle large datasets, it may not be as optimized for big data analytics as Amazon Redshift.
Geospatial Capabilities: PostGIS is primarily focused on geospatial data management and analysis. It provides advanced geospatial functions and supports spatial indexing, allowing users to efficiently perform spatial queries and analysis. Amazon Redshift, on the other hand, does not have native geospatial capabilities. However, it can still store and process geospatial data using custom data structures and functions.
Data Modeling: In Amazon Redshift, data is typically stored in a columnar format, which allows for efficient compression and query performance. It is optimized for analytical workloads and is well-suited for complex data modeling and analysis. PostGIS, on the other hand, follows a more traditional row-based storage model, which may not be as efficient for analytical workloads. However, it provides more flexibility for general-purpose data management and supports a wide range of data types.
Data Import and Export: Amazon Redshift offers seamless integration with various data sources and provides easy-to-use tools for data import and export. It supports ingestion of large datasets from various sources such as Amazon S3, Amazon DynamoDB, and Amazon RDS. PostGIS, being an extension of PostgreSQL, also offers robust data import and export capabilities. It supports a wide range of file formats and provides various tools for data migration and synchronization.
Data Processing Capabilities: Amazon Redshift provides a highly optimized query engine that is specifically designed for analytical workloads. It supports complex query optimizations, parallel processing, and query acceleration techniques such as zone maps and columnar storage. PostGIS, on the other hand, leverages the PostgreSQL query engine, which is well-suited for general-purpose data processing but may not have the same level of optimization for analytical workloads. However, PostGIS provides an extensive set of geospatial functions and operators, which can be beneficial for geospatial analysis.
Cost: Amazon Redshift follows a pay-as-you-go pricing model, where users are billed based on their actual usage. It offers different pricing options based on usage patterns and provides cost-effective storage and processing options for large-scale data analytics. PostGIS, being an open-source extension, is free to use. However, users still need to consider the cost of hosting and managing the underlying infrastructure, which can vary based on the specific deployment.

In summary, Amazon Redshift is a powerful data warehousing and analytics platform that offers scalability, optimized query performance, and seamless integration with various data sources. It is well-suited for large-scale data analytics and complex data modeling. On the other hand, PostGIS is primarily focused on geospatial data management and analysis. It provides advanced geospatial functions and supports spatial indexing, making it a preferred choice for geospatial applications. However, it may not have the same level of optimization for analytical workloads as Amazon Redshift.

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Advice on Amazon Redshift, PostGIS

datocrats-org

Jul 29, 2020

Needs adviceon

Amazon EC2

Tableau

PowerBI

We need to perform ETL from several databases into a data warehouse or data lake. We want to

keep raw and transformed data available to users to draft their own queries efficiently
give users the ability to give custom permissions and SSO
move between open-source on-premises development and cloud-based production environments

We want to use inexpensive Amazon EC2 instances only on medium-sized data set 16GB to 32GB feeding into Tableau Server or PowerBI for reporting and data analysis purposes.

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Detailed Comparison

Amazon Redshift	PostGIS
It is optimized for data sets 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.	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.
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.;Fault Tolerant- Amazon Redshift has multiple features that enhance the reliability of your data warehouse cluster. All data written to a node in your cluster is automatically replicated to other nodes within the cluster and all data is continuously backed up to Amazon S3.;SQL - Amazon Redshift is a SQL data warehouse and uses industry standard ODBC and JDBC connections and Postgres drivers.;Isolation - Amazon Redshift enables you to configure firewall rules to control network access to your data warehouse cluster.;Encryption – With just a couple of parameter settings, you can set up Amazon Redshift to use SSL to secure data in transit and hardware-acccelerated AES-256 encryption for data at rest.<br>	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;Support for importing / exporting ESRI shapefile vector data via both commandline and GUI packaged tools and support for more formats via other 3rd-party Open Source tools
Statistics
GitHub Stars -	GitHub Stars 2.0K
GitHub Forks -	GitHub Forks 407
Stacks 1.5K	Stacks 381
Followers 1.4K	Followers 377
Votes 108	Votes 30
Pros & Cons
Pros 41 Data Warehousing 27 Scalable 17 SQL 14 Backed by Amazon 5 Encryption	Pros 25 De facto GIS in SQL 5 Good Documentation
Integrations
SQLite MySQL Oracle PL/SQL	PostgreSQL

What are some alternatives to Amazon Redshift, PostGIS?

dbForge Studio for MySQL

It is the universal MySQL and MariaDB client for database management, administration and development. With the help of this intelligent MySQL client the work with data and code has become easier and more convenient. This tool provides utilities to compare, synchronize, and backup MySQL databases with scheduling, and gives possibility to analyze and report MySQL tables data.

dbForge Studio for Oracle

It is a powerful integrated development environment (IDE) which helps Oracle SQL developers to increase PL/SQL coding speed, provides versatile data editing tools for managing in-database and external data.

dbForge Studio for PostgreSQL

It is a GUI tool for database development and management. The IDE for PostgreSQL allows users to create, develop, and execute queries, edit and adjust the code to their requirements in a convenient and user-friendly interface.

dbForge Studio for SQL Server

It is a powerful IDE for SQL Server management, administration, development, data reporting and analysis. The tool will help SQL developers to manage databases, version-control database changes in popular source control systems, speed up routine tasks, as well, as to make complex database changes.

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

Liquibase

Liquibase is th leading open-source tool for database schema change management. Liquibase helps teams track, version, and deploy database schema and logic changes so they can automate their database code process with their app code process.

Sequel Pro

Sequel Pro is a fast, easy-to-use Mac database management application for working with MySQL databases.

DBeaver

It is a free multi-platform database tool for developers, SQL programmers, database administrators and analysts. Supports all popular databases: MySQL, PostgreSQL, SQLite, Oracle, DB2, SQL Server, Sybase, Teradata, MongoDB, Cassandra, Redis, etc.

Qubole

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dbForge SQL Complete

It is an IntelliSense add-in for SQL Server Management Studio, designed to provide the fastest T-SQL query typing ever possible.

Related Comparisons

Amazon Redshift vs PostGIS: What are the differences?

Introduction

Scalability: Amazon Redshift is designed for large-scale data warehousing and analytics. It offers massive parallel processing capabilities, allowing it to handle petabytes of data and support thousands of concurrent users. On the other hand, PostGIS is an extension to the PostgreSQL database, which means it inherits PostgreSQL's scalability capabilities. While it can handle large datasets, it may not be as optimized for big data analytics as Amazon Redshift.
Geospatial Capabilities: PostGIS is primarily focused on geospatial data management and analysis. It provides advanced geospatial functions and supports spatial indexing, allowing users to efficiently perform spatial queries and analysis. Amazon Redshift, on the other hand, does not have native geospatial capabilities. However, it can still store and process geospatial data using custom data structures and functions.
Data Modeling: In Amazon Redshift, data is typically stored in a columnar format, which allows for efficient compression and query performance. It is optimized for analytical workloads and is well-suited for complex data modeling and analysis. PostGIS, on the other hand, follows a more traditional row-based storage model, which may not be as efficient for analytical workloads. However, it provides more flexibility for general-purpose data management and supports a wide range of data types.
Data Import and Export: Amazon Redshift offers seamless integration with various data sources and provides easy-to-use tools for data import and export. It supports ingestion of large datasets from various sources such as Amazon S3, Amazon DynamoDB, and Amazon RDS. PostGIS, being an extension of PostgreSQL, also offers robust data import and export capabilities. It supports a wide range of file formats and provides various tools for data migration and synchronization.
Data Processing Capabilities: Amazon Redshift provides a highly optimized query engine that is specifically designed for analytical workloads. It supports complex query optimizations, parallel processing, and query acceleration techniques such as zone maps and columnar storage. PostGIS, on the other hand, leverages the PostgreSQL query engine, which is well-suited for general-purpose data processing but may not have the same level of optimization for analytical workloads. However, PostGIS provides an extensive set of geospatial functions and operators, which can be beneficial for geospatial analysis.
Cost: Amazon Redshift follows a pay-as-you-go pricing model, where users are billed based on their actual usage. It offers different pricing options based on usage patterns and provides cost-effective storage and processing options for large-scale data analytics. PostGIS, being an open-source extension, is free to use. However, users still need to consider the cost of hosting and managing the underlying infrastructure, which can vary based on the specific deployment.

Amazon Redshift vs PostGIS

Overview

Amazon Redshift vs PostGIS: What are the differences?