The fundamentals of Python are critically important in almost any data-related field. Here we will explore how it can help you take your first steps towards being a data engineer.

In my last article, I introduced data engineering as a careerr choice. You can read that article here. In this article, I will be sharing how the Python programming language is a crucial tool in toolbox of a data engineer.

Python fundamentals are important and help you take your first steps to becoming a successful data engineer. In this article, we'll explore topics like writing code using Python syntax; working with different types of data; and performing basic Python operations, such as working with variables, processing numerical and text data, and manipulating lists. Let's dive in.

Why is Python important in the journey of a data engineer?

Based on data scraped from hiring sites, many data engineers affirm that Python as a programming language is useful and most cherished in the journey of being a successful data engineer.

Today, it’s so easy to pick up a new language with all the training contents available for free. So understanding what languages were designed to do and not just how they do it is as important. Python stands out, since anyone, even with no background in tech, can easily pick it up in one week and become good at it. Python is simple to pick up because it’s not very verbose, it’s dynamically typed, and it has a lot of support.

Python is a general-purpose programming language. Because of its ease of use and various libraries for accessing databases and storage technologies, it has become a popular tool to execute ETL jobs. Many teams use Python for Data Engineering rather than an ETL tool because it is more versatile and powerful for these activities.

Python has a huge support system using cloud platforms such as AWS, Azure, and Google Cloud, the tools used for most API's are written in Python, and when creating data pipelines Python is useful. It’s a scripting language and almost everyone has some understanding of how it works.

Python is also ML-friendly, and there are a huge number of good libraries and frameworks from companies like Facebook (Meta), AirBnB, etc. Here, the bigger the supporting company the better, and almost everyone chooses Python.

Big data frameworks are popular for data streaming, data transformation, analytics, and reporting; almost all big data frameworks have Python APIs. You can write code using these APIs and unleash the power of big data. For example, Spark’s Python API, PySpark is very popular among data engineers. Though you can use some of those frameworks without knowledge of any programming language, you will face many challenges and difficulties.

There are many Python frameworks available that make our job very easy. For example, if you need to use web/API development to interact with your database, Python frameworks like Flask, and Django come in handy. They are easy to learn, and very useful if you want to handle your ETL jobs and metadata management through web applications.

Python for Data Engineering is one of the crucial skills required in this field to create data pipelines, set up statistical models, and perform a thorough analysis of them.

How is Python used in Data Engineering?

1) Data Acquisition

Sourcing data from APIs or through web crawlers involves the use of Python. Moreover, scheduling and orchestrating ETL jobs using platforms such as Airflow, require Python skills.

2) Data Manipulation

Python libraries such as Pandas allow for the manipulation of small datasets. In addition to this, a PySpark interface allows manipulation of large datasets using Spark clusters.

3) Data Modelling

Python is used for running Machine Learning or Deep Learning jobs, using frameworks like Tensorflow, Keras, Scikit-learn, and Pytorch.

4) Data Surfacing

Various data surface approaches exist, including the provision of data into a dashboard or conventional report, or the opening of data as a service. Python for Data Engineering is required for setting up APIs to surface the data or models, with frameworks such as Flask and Django.

Let’s check some of the Top Python libraries for Data Engineering

• Pandas
• Psycopg2, pyodbc, sqlalchemy
• Elasticsearch
• Great Expectations
• SciPy
• BeautifulSoup
• Petl
• pygrametl

Conclusion

In this article, you learned about the significance and importance of Python for Data Engineering, as well as some fo the ways it integrates with other applications and tools. This article also highlighted some fo the more common libraries used in Data Engineering, and explored various benefits and use cases of Python for Data Engineering.

If you're ready to try out some of these tools, why not try using Python to tackle one of our knowledge competitions, or check out how to build a computer vision model with PyTorch.

About the author

Odeajo Israel is a Google TensorFlow Certified professional with four years of experience in the analysis sector. He helps organisations make data-driven decisions and design metrics specific to their organisation. Israel is also a Zindi ambassador for Nigeria. He is enthusiastic about topics such as deep learning, machine learning, big data, and artificial intelligence. In Nigeria, he is one of the co-organisers and facilitators of the AI movementt. He leads meetups, workshops, and events with the goal of constructing a community of data scientists who can tackle local problems. You can reach him on LinkedIn.