Released as an open-source project in 1996, PostgreSQL is a powerful, open-source relational database management system (RDBMS).
In the decades since, it’s undergone many changes in its development and increasingly included more advanced features. It’s since become a go-to tool for developers due to its focus on extensibility and SQL compliance. These features have also made it a great choice for data analysts with their everyday data management needs.
In this article, we will take a look at the history of how PostgreSQL came to be, and some of the benefits and constraints that might make it a better fit for some applications.
- What is PostgreSQL: History
- Benefits of PostgreSQL
- Constraints of PostgreSQL
- What is PostgresSQL: Data analyst use cases
- Summary and next steps
Ready? Let’s learn about one of the most popular RDBMS systems around!
1. What is PostgreSQL: History
PostgreSQL was born in the 1980s. A group of computer scientists led by Michael Stonebraker, a key figure in database research and development, at the University of California were working on a new database project that became a successor to their earlier one called “Ingres”. They called this “Post Ingres”, which then became renamed in 1996 as PostgreSQL to emphasize its focus on structured query language (more commonly known as SQL) support.
In the 1980s, there were many different competing visions of what a database should look like. Each had their own unique limitations and weaknesses. Stonebraker’s team was interested in building extensibility into a database.
As it was one of the first systems to implement object-relational databases, PostgreSQL could allow many data types to be stored and also supported user-defined data types. This extensibility allowed PostgreSQL to improve on its contemporaries that required pre-defined data structures, which meant that users were locked-in to what the database could record once it was designed and launched.
Stonebraker’s team added SQL support to Postgres in 1994 as this meant that it could be used by many more people. This proved to be an important factor in PostgreSQL’s rise since, as SQL remains the most popular language used to interact with databases.
PostgreSQL’s release as an open-source software in 1996 shaped its profile and development trajectory. The public could now contribute to the project, which accelerated its development. A global community of developers emerged to help update and improve the system.
These reasons are why PostgreSQL is best known for not just its continued extensibility, but its robustness and rich set of features that remain competitive against commercial databases.
2. Benefits of PostgreSQL
There’s a lot to love about PostgreSQL, and we’ll take a look at some of the reasons why it remains such a popular RDBMS.
The first reason revolves around its extensibility, which was the feature that first differentiated it from rival databases in the 1980s. It not only supports a broad range of data types, it allows you to create your own data type if you need to tailor the database for your own needs.
It also allows you to create custom functions, operators, and aggregate methods. More advanced users can even define their own procedural language and indexing methods. This flexibility lies at the core of PostgreSQL and allows it to be reworked for a wide range of use cases, which we’ll dive into more deeply later.
It’s also an open-source ACID-compliant database. Open-source software refers to software that anyone can use, modify or distribute, including for commercial purposes.
Commercial databases can be very costly to license. Hence, adopting PostgreSQL can lead to enormous cost-savings for startups and large companies.
Another benefit of using open-source software is that because the developer community is continually improving and fixing problems with it, users rarely have to wait too long before a solution to an error arises—and done for free too.
ACID stands for atomicity, consistency, isolation, and durability. These are standardized properties that ensure database transactions are recorded consistently. This is critical for ensuring data integrity, especially for industries such as healthcare that rely on the accurate and reliable processing of any database transactions.
Furthermore, PostgreSQL also works across operating systems including MacOS, Windows, and Linux. This ensures that a high proportion of any company’s developers can interact with PostgreSQL, and also provides them the flexibility to use the language they know best.
However, if you haven’t learnt any of these languages yet, fret not: check out our helpful guides that explain what SQL is and how you can learn it quickly to start retrieving data in your next project!
3. Constraints of PostgreSQL
While PostgreSQL is a powerful and flexible database system, it has several constraints that users need to consider before choosing to work with it.
PostgreSQL may not provide the best performance. It’s optimized for complex operations and transactions, and the tradeoff for ensuring data integrity and extensibility means that this impacts performance.
This becomes evident in situations where you might need to read heavy loads or read data at very high speeds. PostgreSQL also lacks native sharding: sharding is a popular technique used to spread data across many databases which can help improve performance when databases can get very large.
Since most applications will not need the fastest or most powerful database performance, this may not be a concern. But if optimal performance is a factor, you might want to consider other databases such as MySQL or NoSQL databases instead.
PostgreSQL’s rich set of features mean that you can use it for many applications, but this can present a challenge for beginners to learn. In fact, the tool can present a challenging learning curve due to its many advanced features and capabilities.
Further, its focus on extensibility and standards compliance can make it seem more complicated compared to other databases out there. If you are a beginner, this should not deter you from trying it out since the learning curve is often worth the access to PostgreSQL’s powerful and flexible features. You can also find many helpful tutorials online, since it’s a popular open-source database.
Finally, PostgreSQL uses much more resources than many alternative databases. Because it uses a multi-process instead of a multi-threaded architecture, this means that each connection to the database is handled by a separate backend process.
This results in the consumption of a significant amount of memory, which happens when databases have to handle a large number of connections all at the same time. The higher memory usage makes this a resource-intensive database that may be too costly for some businesses to justify.
4. What is PostgreSQL: Data analyst use cases
Now that we have an understanding of how PostgreSQL works, let’s explore two of the key ways that data analysts can use it to navigate the complex and exciting world of data!
Building a data warehouse
Before you can run any business intelligence analytics, you will first need to collect and store the data in one database. Data warehousing refers to the process of collecting and combining data from different sources, including other databases, into a single database. It makes it much easier for data analysts to run queries since they can interact with just one single database.
PostgreSQL makes this process much easier for several reasons. First, it can handle lots of different types of data, so collecting data from different sources that use different data types will not be a problem. Second, PostgreSQL allows for extract, transform, and load processes (ETL) which is an industry term that refers to the process of extracting data from different sources (e.g. CSV files, web APIs), transforming them using SQL queries (e.g. data cleaning and aggregation), and loading it into your PostgreSQL database.
For example, if you work as a data analyst for an insurance company, a common workflow might involve extracting claims data from the insurance platform and external APIs, cleaning and aggregating key statistics in PostgreSQL, and then loading it all into one data warehouse that can be made available internally for further analysis.
Reporting and analysis
PostgreSQL contains features that make reporting and analysis relatively easy and straightforward. It allows for the creation of complex queries, where you can extract data from different sources or tables, calculate aggregations, and produce summary statistics.
For more advanced use cases, if you have access to real-time data, you can use PostgreSQL’s real-time data processing feature. This allows you to set up a notification every time a data entry is recorded that meets pre-determined filters.
Let’s say you are a data analyst for an online shoe company, and the leadership team has requested a detailed report on sales performance in the last three months, but they want this within a few hours. With PostgreSQL, you can quickly generate the key statistics you need, allowing you to go the extra mile to consider deeper analysis such as identifying areas for potential growth.
5. Summary and next steps
PostgreSQL is a highly-flexible and reliable database management system that serves a wide range of applications.
Today, we’ve taken a look at how it can meet the needs of both small projects, as well as large and complex enterprise use cases. The rich set of features it offers has made it a highly attractive choice for businesses and developers globally.
If you’re interested in learning more about how database use and management can help you in your data analytics career, we recommend reviewing some of the key concepts about PostgreSQL before trying out your first project:
Weigh the benefits and constraints first
Always consider the strengths and weaknesses of any platform or framework you want to implement. PostgreSQL may be highly flexible, open-source, ACID-compliant, and useful for multiple languages and platforms.
But it doesn’t always offer the best performance for data retrieval and storage, and it can be intimidating for beginners to learn. Think about the resources available at your company, or your project’s needs, before settling on any RDBMS.
Think about other use cases
We talked about how you can use PostgreSQL to build a data warehouse for data analytics, and use its reporting features to process data quickly. But you can find many other ways to leverage PostgreSQL. You might be able to find a project that can benefit from the way PostgreSQL excels with managing large amounts of data generated in real-time.
If the world of business analytics interests you but you don’t know where to start, why not try CareerFoundry’s free 5-day data analytics course? It covers the basics of data analytics as a field and will give you a good idea of whether or not it’s a career path you’re interested in pursuing further.
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