Is Python The Right Language For Your Business
According to Seagate UK, “By 2025, there will be 175 zettabytes of data in the global data-sphere.” Companies are placing a high premium on data, namely how to better use it to their advantage.
Data once seemed an innocuous thing, but it is now possible to use it to gauge the current status of the business, forecast the future, inferring the sorts of people your typical customers are, avoid potential threats, and develop new goods; though none of this would be possible without Data Engineering.
Data Engineering is the linchpin which holds many of these activities together, and data engineers wouldn’t get terribly far without a language through which they can analyse and interpret these seemingly endless streams of data, such as Python. Python is one of the world’s most popular programming languages, it is an open-source, object-oriented programming language that is easy to learn, with readable syntax. Python also includes an ocean of libraries that can serve any number of uses in the field of data engineering.
While not a sales pitch, the point of this article is simply to expound on some of the virtues of Python, in the hopes that anyone who may be hitting some consistent snags in their data engineering pipeline can skim the list and find that Python could very well be the answer they need.
Why is Python so popular?
Python is a universally popular programming language. Companies often find themselves sitting on mountains of potentially lucrative data, data which can’t be harvested and sorted without Software Engineers being able to design tools for handling all of the data as efficiently as possible; tools which the Data Engineers will have to utilize in the actual gathering of data. The way such data is modelled, stored, protected, and encoded must then be considered; however, all of this work is really to no avail if your Data Engineers can’t quickly parse what your Software Engineer has written, this is why knowledge and implementation of a core programming language like Python is a must.
Python is not only easy to use, but versatile. Due to its ease of use, and plethora of libraries for accessing databases and storage technologies, Python has also been found useful in performing ETL (Extract, Transform, Load) jobs. For things such as ML (Machine Learning) and AI, Python is the bonafide lingua franca of the field. Due to being so common, there is little risk in your code being lost in translation.
Python is also popular for its use in technologies, such as Apache Airflow, and libraries for popular tools such as Apache Spark. Should you use tools like these in your business, then you should already know about Python, and if not, you should certainly brush up on it, as knowing what language(s) you utilize is far more important than it may seem.
Python or Java?
Java, like Python, is an immensely popular language; in fact, you may even be using Java currently. The goal of this section is not to deride Java; it’s simply to outline the difference between the two in case Python may be more suited to your needs without you knowing.
1. Ease-of-Use: Both languages are expressive and simple, with a possibility to achieve high functionality if put to task correctly; however, Python is undoubtedly more user-friendly and concise. Python is far and away less intimidating, which can go a long way when leaping into the already boundless and intimidating world of Data Engineering.
2. Wide Applications: Perhaps Python’s biggest benefit over Java is its sheer versatility. Java is the specialized player with a few tremendous strong-points, but Python is a language for all seasons who, while not astounding at many things, is good-to-great at effectively everything.
3. Learning Curve: Though both Java and Python have bustling support communities, Java is ultimately more complex, due to its high-level functional characteristics. This not only makes the initial adoption period more difficult, but makes the bath to mastering the language that much more difficult and potentially frustrating. Now, obviously the trek is worth it should Java offer something which Python can’t, but assuming you only need some form of simple, intuitive logic, then Python should be your go-to.
Yeah, but what can Python actually do?Data can be used to answer any number of critical business questions, but how exactly can Python assist in the acquisition and sorting of said data?
1. Data Acquisition: Python is excellent at sourcing data from APIs, or through Web Crawlers. Also, scheduling and executing ETL jobs using platforms like Airflow, requires Python.
2. Data Manipulation: Python libraries, such as Pandas, allow for the manipulation of small datasets. Python also provides a PySpark interface which allows the manipulation of large datasets using Spark clusters.
3. Data Modelling: Python can be, and often is, used for running Machine Learning or Deep Learning jobs, using such frameworks as Pytorch, Scikit-Learn, Tensor/Keras. This broad application is what made it key for communicating effectively between teams.
4. Data Surfacing: Many data surfacing approaches exist, such as the provision of data into a dashboard or conventional report, or the opening of data merely as a service. In Data Engineering, Python is required for setting up APIs in order to surface the data, or models, with frameworks such as Django, or Flask.
Should you be in the market for a more complex language in which to write a more complex program or system, then you should continue to shop around; however, should you instead be looking for an easy-to-use, easy-to-start, easy-to-learn widely integrated and versatile language in order to simply sort through the mountain of data piling up at your doorstep, then why not try out Python?