Database Review – Why Should You use PostgreSQL for Data Science Applications?

Data Sciences have evolved a lot over the last several years, whereas many data scientists tend to work with CSV files yet, which is not the best choice. Avenues like Python Panda library let you load data directly from CSV files, but it comes with several constraints in terms of execution. Say, for example, they usually will not connect to a database, which further requires you to generate another new CSV file extract each time when you have to update the data. When the data volume is huge in terms of big data, this approach is nearly impractical.

Relational databases offer support and needed agility for big data repositories, and PostgreSQL is one of the leading RDBMS with big data support. PostgreSQL is effectively designed to meet the need for handling large datasets, which makes it a perfect match for computation intensive data science projects too. Here, in this article, we will cover the usage of Postgres in data science.

The role of data science

Data science focuses on processing big data and thereby helps organizations to gain actionable insights based on their data. Say, for example, with data-drive insights, an enterprise can derive its marketing strategy or change its offers based on the live market trends. Data science is a highly diverse field now based on the huge volume of data it manipulates, and there are many core skills that the data scientist needs to acquire to handle data science projects well. These are:

• Programming Skills
• Math and Statistical skills
• Good technical knowhow, and
• Analytical skills.

PostgreSQL database

PostgreSQL is one of the second-generation relational database management systems, open-source and offered by PostgreSQL Global Development Group. It is a consortium of companies along with many individual contributors, which offers PostgreSQL and offers support. It is now an extensive Software-as-a-Service (SaaS) solution, which enables running of it on the cloud and on-premise. The key features of the PostgreSQL database are:

• Free licensing: PostgreSQL can be freely downloaded, used, modified, expanded, and distributed.
• Complex and advanced query support: Another major feature of Postgres is its advanced capabilities to process even complex queries. What is meant by complex queries is the requests you make that go far beyond the fundamental SQL requests like WHERE, SELECT, or CREATE.
• Multi-version concurrency control: It is an advanced feature of PostgreSQL, enabling various users to write and read information onto a given database simultaneously.
• User-defined types: PostgreSQL users can customize the database functions to define data types individually. This is a data science-centered feature where the data scientists who work on PostgreSQL with unknown and known data make it easier to combine two or more types of data into one effectively. It enables addressing complex problems with a myriad of data coming from various sources.
• High Compliance of SQL Standard: PostgreSQL can meet 80% of the mandatory compliance features required to meet the standards. Ensuring compliance with the SQL standards is one of the priorities of Postgres from the very beginning itself.
• Solid community support: As RemoteDBA experts point out, Postgres maintain a very large and strong community around it. There are many contributors and supporters too who are dedicated to expand and develop the platform further. PostgreSQL also has extensive documentation and dedicated support through the forums.
• anguage support: We have already discussed that being a second-generation relational database, PostgresSQL can support all widely used programming languages like Python, Java, C, etc. it can support JSON queries for NoSQL too.
• Multi-environmental support: Another very important characteristic of the PostgreSQL database is the support offered for both on-premise and cloud environments. This cross-environment or multi-environment support makes PostgreSQL widely acceptable for businesses of all sizes. Most organizations now use Postgres at a hybrid cloud, which is a mix of on-premise and cloud with a fine balance.

PostgreSQL pros and cons in data science

Even being a relational DB, PostgreSQL proved to be effective in supporting big data applications with the addition of JSON-B for documents and PostGIS for the geolocation functions. It also lets the users adapt effectively to the platform for handling their increased workloads. PostgreSQL combines both transactional and analytical capabilities in a hybrid model as HTAP (transactional/analytical processing). With this, the database can effectively perform both OLAP (online analytical processing) and OLTP (online transaction processing) simultaneously. Organizations can also use the HTAP technologies on Postgres for managing information from the IoT devices and other applications.

Lately, Postgres has become much popular among the data scientists of various industries due to the flexibility and scalability it offers. However, unlike the conventional RDBMS< Postgres, it does not store the data in columns, making it tough for the large traditional data warehouses to do data processing. Here are some other pros and cons of PostgreSQL in data science.

Pros

• Postgres is SQL rich: With its emphasis on being SQL compliant, Postgres tend to support almost every SQL syntax.
• Support for unstructured data: Postgres can also support unstructured NoSQL data like JSON, XML, and HStore, etc.
• Parallel queries: Postgres may let all the cores in a processor simultaneously. This is a critical plus in data science.
• Declarative partitioning: You can custom specify how to divide a table into parts, called portioning.

Postgres cons

• No scope of compression: Having only limited space may significantly limit some data science analysis performance.
• No Columnar structure: Databases for analytical purposes usually store data in columnar form instead of row-based. PostgreSQL lacks the columnar table structure, which sometimes makes data integration impossible.
• No machine learning capabilities in-built: This is one of the major drawbacks of Postgres, whereas machine learning acts as the backbone of big data.

All in all, PostgreSQL can provide a very low-cost yet powerful processing platform for data science projects. However, the biggest issues can be not featured for data compression and limited support for machine learning. However, you can work around these challenges by uploading data in batches and running the DB only in the cloud environment.