Programming Languages | At a Glance

Within the data and analytics space we have seen a lot of software that is low to no code popping up. Although this functionality helps make advanced analytics and various other data manipulation activities more accessible, the art of writing code isn’t going anywhere and is as valuable as ever. There a quite literally thousands of programming languages but there are a select few that are considered commonplace within the data and analytics space. This blog explores the five programming languages used most frequently by Billigence consultants. 

Five Common Programming Languages

SQL

SQL (Structured Query Language) is a standard programming language used for managing and operating relational databases. It is a critical tool in the data sector because it allows for the storage, retrieval and manipulation of large amounts of data in an organised and efficient manner. SQL is used for a variety of tasks including data analysis, data validation and data updates, making it a fundamental tool for organisations to make informed decisions.

Python

Python is a high-level programming language that is widely used in the field of analytics because of its versatility, ease of use and strong support for data analysis and visualisation. It can perform a wide range of tasks, from data cleaning and pre-processing to advanced machine learning and deep learning algorithms. Its simple and readable syntax makes it accessible to users with varying levels of technical expertise. This means developers can quickly and easily prototype, test and implement data-driven solutions without having to worry about complex or low-level technical details.

Python has a large and active community of users and developers, which has resulted in the development of a variety of libraries and tools specifically designed for data analysis, machine learning and scientific computing. Python also has an API for Apache Spark called PySpark. Pyspark is a library that allows a user to write Park applications, which are used for processing lots of data (both batch and streaming) using a cluster of machines.

R

R is a versatile programming language and software environment for statistical computing, performing complex data analysis quickly and visualisations. It is widely used in the field of analytics for its ability to perform complex data analysis tasks including statistical modelling, data visualisation and machine learning. R is open source and, much like Python, there is a large community of users and developers who have created readily available packages and tools for others to use. R’s ability to handle and manipulate large amounts of data and its user-friendly syntax makes it a popular choice among data professionals.

Scala

Scala is a modern programming language that is highly expressive and supports both object-oriented and functional programming paradigms. It is a versatile tool that is often harnessed for data processing and analysis. This language is unique as it is a statically typed and runs on the Java Virtual Machine (JVM), making it highly scalable and efficient for big data processing tasks. By utilising Scala data professionals can express complex data processing and analysis tasks in a concise and readable manner, while also taking advantage of the scalability and efficiency offered by the JVM.

Java

Java is a widely used programming language known for its portability, versatility, scalability and security. It’s known as a suitable choice for developing large-scale data processing systems. It is a statically typed and object-oriented language that provides strong type safety and is both highly reliable and stable, making it an ideal choice for organisations looking to extract insights from large amounts of data.

Java has its own Java Virtual Machine (JVM) which allows for Java code to be optimised for various hardware and software environments. They also have various tools performing data manipulation, storage/retrieval and integration with big data processing frameworks including:

  • Java Database Connectivity (JDBC) API: Provides a standard way to access relational databases.
  • Java Persistence API (JPA): Provides a standard way to map Java objects to relational databases.

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