In this guide, we’ll cover how to implement common data analysis use cases on PostgreSQL and show you how to scale it 📈 as your data grows. From fundamental querying to advanced aggregation and grouping strategies, we’ll explore how PostgreSQL serves as a robust platform for processing data.

Jan 19, 2025 · Learning from real-world examples and case studies is essential to understanding how PostgreSQL excels in data analysis and query optimization. This section showcases practical applications, challenges faced, and solutions implemented using PostgreSQL.

Jun 14, 2023 · This article showed how to do preliminary data analysis using PostgreSQL. Many of the built-in functions used for data analysis are listed under Aggregate Functions in the official PostgreSQL documentation. The documentation also lists a …

Feb 10, 2025 · In this blog post, we will compare the main techniques and approaches for running data analytics with PostgreSQL: Each of them has their pros and cons, so we will take a look at them and see which one may be the best for your use case. The PostgreSQL ecosystem has matured and become capable of dealing with analytical workloads in recent years.

In this article, we’ll explore the best practices for overcoming these challenges and optimizing the data analysis process within PostgreSQL. By following these guidelines, you can enhance performance, streamline management processes, and improve security, extracting maximum value from your data.

May 23, 2021 · In this part of the Series, I’m going to perform Exploratory data analysis with PostgreSQL.

Timescale lets you handle both transactions and analytics in your existing PostgreSQL database, eliminating the need for ETL (extract-transform-load) pipelines and multiple systems. In this blog post, we explain how Timescale’s unique approach builds on PostgreSQL, enabling it to handle the challenges of live data.

Apr 10, 2019 · What I hope to teach you in my session at OSDC is all the interesting things PostgreSQL has to offer to make your big data cleaning and analysis easier. Let me give you the outline (and some links) on what we are going to cover in the session. Spatial Data – PostgreSQL has the gold standard in spatial data extensions – PostGIS.

PostgreSQL, an open-source database system, excels in data analysis with rich functionalities like JSON support and diverse indexing options. It's ideal for big data and analytics due to SQL standards, extensibility, and robust performance. PostgreSQL is pivotal for enterprises seeking advanced data analysis solutions.