What You Need to Know about PostgreSQL for Analytics

We did the research and pulled together some useful articles on PostgreSQL so you don’t have to! Read this page to find out some great information on this relational database so you can make your own decisions on data storage and building your analytics stack.

What is PostgreSQL?

PostgreSQL is an open-source object-relational database management system. It is ACID-compliant and transactional and uses and extends the SQL language with features that scale data workloads.

PostgreSQL has been maintained as an open-source database for over 30 years by developers and volunteers in its community, earning its reputation for one of the most commonly used, reliable and well maintained open-source databases with some of the best community support.

For more on PostgreSQL, refer to the official website, documentation, and community.

Should you use PostgreSQL for an Analytics Database?

Data type and structure

The most important question in choosing a database for your business data, is what kind of data are you storing? If your data is structured into one or more tables of columns and rows, a relational database will fit your needs. PostgreSQL is a relational database but that offers an object-oriented database model, in which objects, classes and inheritance are supported in the database schemas and query language.

PostgreSQL works with a variety of data types, and if you are pulling multiple data sources into one place, PostgreSQL allows you to put those different data sources into one dedicated schema.

Database size and performance

If you are under 1 TB of data, PostgreSQL will provide a good price to performance ratio, but it will slow down at about 6 TB.


If you have engineers available for database setup, but less so for ongoing maintenance, PostgreSQL provides a good option as there are less data pipeline fixes required. Additionally, PostgreSQL uses SQL, which is well-known among analysts and engineers and is easy to learn.

If you are comparing PostgreSQL against another open-source relational database, some of our favorite comparisons are:

The Benefits of Using PostgreSQL for Analytics

As a database for business analytical reporting, there are many benefits to choosing PostgreSQL, we have listed just a few below:

PostgreSQL provides an extensive set of querying capabilities, such as combining queries, window functions, and lateral queries. Other relational databases have similar options with workarounds but in PostgreSQL this means less complicated queries (which also means better performance!).

PostgreSQL has a unique feature called Foreign Data Wrapper, which lets you create foreign tables in a PostgreSQL database that are proxies for another data source. This allows you to write a query that combined data from multiple PostgreSQL databases.

Note: Foreign Data Wrappers are not currently supported in Chartio.

PostgreSQL is extensible, meaning it is highly customizable through existing or self-made plugins. You can define your own data types, index types, functional languages, etc.

As an open-source database, PostgreSQL has great community support and extensive documentation.

Hear from others on why to choose PostgreSQL:

The Limitations of Using PostgreSQL for Analytics

PostgreSQL tends to be less efficient for large datasets, and becomes an issue when scaling up your database.

Similarly, if security, licensing and support are important to your business an option such as MySQL will be more suitable as MySQL is owned by Oracle and offers paid versions for commercial use and enterprise support.

Hear from others on the limitations of PostgreSQL:

Running Data Analytics on PostgreSQL

Choosing PostgreSQL for your reporting database is only the first step to using your database for analytics.

If you are moving data from multiple sources into PostgreSQL you will need to consider if you will build your own data pipeline, but this is an extensive process dependent on your engineering resources. If you are unable to write your own extraction, data collection and transformation processes, you can easily use one of the ETL tools on the market to pipe your data into PostgreSQL.

Some recommendations for moving your data into PostgreSQL:

A different consideration is if you want to move your PostgreSQL into a warehouse to consolidate with your other data sources. In the same sense that there are many ETL processes and tools available to pipe data into PostgreSQL, there are also many options for piping data out of PostgreSQL.

We recommend the following for learning how to move your data out of PostgreSQL:

Finally, you will want to choose how to visualize the data insights you are pulling from PostgreSQL. PostgreSQL integrates well with almost any business intelligence, reporting and visualization option, so choosing your analytics tool depends more on your business needs rather than any PostgreSQL limitations.

Learn more about visualizing PostgreSQL with Chartio: