Top 5 Reasons PostgreSQL Works for Data Analysis (and Data Analysts!)

Why do analysts love PostgreSQL for data analysis? Learn why this database management system is so beloved of database professionals and data scientists alike!

If you work with data, you know that data analysis requires the efficient storage, management, and retrieval of large datasets. Thus, data analysts prefer to use relational databases that are well-known for their robustness, efficiency, and stability.

Relational databases work with a database management system (DBMS), which allows the creation, management, and manipulation of relational databases. DBMSs also ensure that data is organized, secure, and accessible when needed. For more details on this, read Luke Hande’s great article What Is an SQL Database?.

Today’s most popular DBMSs include MySQL, Microsoft SQL Server, Oracle, SQLite, and PostgreSQL, among others. In the context of data analysis, PostgreSQL is a popular choice among data analysts. In this article, I will demonstrate why many data pros choose PostgreSQL for data analysis.

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PostgreSQL for Data Analysis: A Wise Choice

There are 5 main reasons data analysts prefer PostgreSQL:

1.    Reliability and Stability

What if some transactions on a database were to fail without warning? What if we couldn’t expect databases to reliably record every bit of data they receive? For most businesses, this would be a critical problem. Imagine you run an e-commerce site. A client makes a purchase, the client’s bank approves the transaction, but your core system only stores part of the payment. Even worse, imagine the same problem with a banking application, i.e. a customer makes a deposit and your database doesn’t store it. The consequences to the customer and to the bank could be huge!

Since data integrity and reliability are critical, most relational databases are designed to support ACID compliance. ACID means that information is:

• Atomic – Each transaction is a single unit that either completely succeeds or is not performed at all. This ensures that no commands are partially processed and maintains data integrity in the event of a system or power failure.
• Consistent – The data stored in a database must meet certain defined rules and must be stored in a stable state.
• Isolated – If the database is handling multiple transactions at the same time (and they often are), each transaction impacts only the record(s) directly involved in the transaction. Multiple transactions can happen simultaneously and independently.
• Durable – The data in a database is stable; it doesn’t degrade or change over time (unless, of course, the database operator makes the change).

These properties help guarantee reliable transaction processing in a database management system (DBMS). In the context of data analysis, PostgreSQL is a favorite relational DBMS because it is fully ACID compliant; it ensures that transactions are processed reliably even in the case of system failures.

2.    Advanced Features

Did you know that the majority of relational DBMSs allow the creation of custom data types? First, you declare the new type and then you can use it just like a standard data type. PostgreSQL is highly extensible; it allows users to create numerous custom data types. This includes composite types, range types, and enumerated types, among others.

Imagine that we want to store RGB (Red, Green, Blue) colors in a PostgreSQL database. First, we declare the new custom type; then we use it when we create the colors table. After that, whenever we add a new row to this table (or to any other table in the same database), we can insert new values that use this special data type.

CREATE TYPE rgb_color AS (
    red   INT,
    green INT,
    blue  INT
);

CREATE TABLE colors (
    name TEXT,
    value rgb_color
);

INSERT INTO colors (name, value) 
VALUES (French Flag Blue', ROW(0, 35, 149));

It’s as simple as that! By the way, if you’d like to see similar queries, check out Nicole Darnley’s article Top 7 Advanced SQL Queries for Data Analysis.

In the same way, PostgreSQL allows custom or User-Defined Functions (UDFs). These are like regular functions, but you can create your own using a process similar to custom data types. Depending on how you create it, this function is available in the current schema or in all schemas.

To create your own function, you first define the custom function and its parameters. To use your custom function, simply call it in a SELECT clause.

Imagine that you need a custom function that calculates a total price, including a tax rate. This is how you could do it using PL/pgSQL (PostgreSQL's procedural language):

CREATE OR REPLACE FUNCTION calculate_total_price(price NUMERIC, tax_rate NUMERIC)
RETURNS NUMERIC AS $$
BEGIN
    RETURN price + (price * tax_rate);
END;
$$ LANGUAGE plpgsql;

SELECT calculate_total_price(100, 0.08) AS total;

Awesome, isn’t it? Let’s go further with custom operators! If you have studied pure mathematics algebra, you may know that the common arithmetic operators we use in everyday life (addition, subtraction, multiplication, and division) are just the tip of the iceberg! There are a lot of other mathematical operators – you can even define custom operators.

A similar feature is available with most relational DBMSs: they allow users to create custom operators for existing or custom data types. This feature is particularly useful when working with complex data types or when you need specialized operations that are not covered by standard operators. PostgreSQL is one of the most flexible and extensible relational DBMSs when it comes to custom operators.

Let’s try the following example. We want to create a new operator '#>' that compares the lengths of two strings. First, we declare a new custom function and its operator:

–-Create the function
CREATE OR REPLACE FUNCTION length_greater_than(text, text)
RETURNS BOOLEAN AS $$
BEGIN
    RETURN length($1) > length($2);
END;
$$ LANGUAGE plpgsql;

–-Create a custom operator for the function
CREATE OPERATOR #> (
    LEFTARG = text,
    RIGHTARG = text,
    PROCEDURE = length_greater_than
);

Now we can use the new operator in a query:

SELECT 'learnpython' #> 'learnsql' AS result;

Still hungry for more PostgreSQL queries? Challenge yourself with the ones in Gustavo du Mortier’s 19 PostgreSQL Practice Exercises with Detailed Solutions!

3.    Community and Ecosystem

PostgreSQL has one of the strongest and most active communities among all DBMSs. The highly motivated members of the PostgreSQL community are very productive: they deliver tons of quality content (tutorials, articles, courses, etc. ). All over the world, you can find UGs (User Groups) that organize Postgres meetups, workshops, and conferences. The biggest events are the PostgreSQL Conference Europe and the PGConf US .

It’s also important to mention PostgreSQL's official documentation. This is one of the most detailed and comprehensive resources about PostgreSQL available. It covers everything from installation and configuration to advanced topics like custom functions. Furthermore, the documentation includes many tutorials and guides that help users at all levels; from beginners setting up their first database to experienced developers implementing complex queries.

You can also find great books about PostgreSQL! I recommend you read Jakub Romanowski’s article Best Books for Learning PostgreSQL for some quality picks.

Finally there’s the PostgreSQL ecosystem, which supports a wide range of extensions that improve Postgres’ functionality. Popular extensions like PostGIS (for geospatial data), pgAudit (for auditing), and Citrus (for scaling) are developed and maintained by the community and commercial entities.

The PostgreSQL ecosystem also includes many third-party tools for backup, monitoring, and database management. Tools like pgAdmin, DBeaver, and pgBackRest are widely used and well supported. PostgreSQL also enjoys strong support from Cloud providers like Amazon (with RDS for PostgreSQL), Google (Cloud SQL for PostgreSQL), and Microsoft (Azure Database for PostgreSQL). These integrations provide managed services that make it easier to deploy and scale PostgreSQL in the Cloud.

To illustrate this section, let me tell you about the Stack Overflow Survey 2024. Among database pros, PostgreSQL is undoubtedly its most popular database for 2024. And it’s growing: from 33% in 2018 to almost 50% in 2024, PostgreSQL has a great future!

Stack Overflow Survey 2024

Jakub Romanowski went through the Stack Overflow Survey 2024 in his article 2024 Database Trends: Is SQL Still the King? His conclusion? Relational databases are still trendy and PostgreSQL is the boss.

4.    Performance and Scalability

PostgreSQL is data analysts’ favorite DBMS in terms of performance and scalability.

First of all, there’s Postgres’ optimized storage engine, which is designed to manage large volumes of data efficiently. It uses an advanced system of page-level storage, with features like multiversion concurrency control (MVCC) that allow for high transaction throughput without locking rows during reading. MVCC ensures data consistency and isolation in a concurrent environment where multiple transactions are executed simultaneously.

Next, we have PostgreSQL’s support for table partitioning. This is a database design technique that manages large tables by dividing them into smaller pieces called partitions. Each partition is a subset of the data in the table and is considered an individual entity by the database system. Partitioning can improve the performance, manageability, and availability of large datasets. PostgreSQL supports several partitioning strategies, including range, list, and hash partitions.

Finally, PostgreSQL allows parallel queries, which improves database performance by using multiple CPU cores to process large and complex queries. This feature is particularly beneficial for data analysis, since it can handle operations that implicate scanning large tables or performing complex joins.

5.    Security and Compliance

PostgreSQL is known for its strong security and compliance features, which make it a perfect choice for organizations that require strict data protection and regulatory compliance.

Role-based access control (RBAC) is one of Postgres’ fundamental security features; it allows administrators to manage permissions and control access to the database. There are various roles and privileges that can be assigned. In PostgreSQL, a role can represent a user or a group of users. Roles can be given specific privileges to perform actions on database objects like tables, views, and functions. Privileges determine what actions a role can perform, e.g. SELECT, INSERT, UPDATE, DELETE, or EXECUTE.

PostgreSQL also has data encryption, which helps protect sensitive data both at rest (data stored in the database) and in transit. For encryption at rest, the pgcrypto module can be used to allow the encryption and decryption of data at the column level. This way, sensitive data can be encrypted directly within the database.

Find Out More About PostgreSQL for Data Analysis

In this article, we have seen why data analysts frequently choose PostgreSQL for data analysis. If you have the opportunity to work with PostgreSQL, I encourage you to explore the features we’ve mentioned; they are fascinating!

If you are interested in starting a data analyst career, don’t miss Kateryna Koidan’s Roadmap to Becoming a Data Analyst. If you need to learn PostgreSQL, we recommend going through SQL from A to Z in PostgreSQL. Just create a free account and complete the first few exercises to see how it looks and feels. Then you can decide if it meets your needs.

Thanks for reading this article; I really hope you liked it! See you in the next one!