Data Engineering 102: Introduction to Python for Data Engineering
In the previous section of my articles in the ongoing Data Engineering Mentorship Program by Data Science East Africa and Lux Academy, I introduced the concept of the importance of data engineering, click here to read more.
In this article, I will be sharing thoughts on the use of python programming language for the data engineering journey.
Python fundamentals are important and help you take your first steps to becoming a successful data engineer.
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.
Why is python important in the journey of data engineering?
based on research and use cases, many data engineers affirm that python as a programming language is useful and most cherished in the journey of being a successful data engineer.
Python has a huge support system using cloud platforms such as AWS, Azure, and Google Cloud, the tool used for API are written in python, When creating pipelines for Dataswarm (it’s like Airflow) python is useful. It’s a scripting language and everyone knows it basically.
Python is simple to pick up because it’s not very verbose, it’s dynamically typed and it has a lot of support.
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, anyone even with no background in tech can easily pick it up in one week and become actually good at it.
ML friendly, good frameworks from Facebook (Meta), AirBnB, etc. Here the bigger the supporting company the better and almost everyone chooses python.
Big data frameworks are so 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 some web/API development to interact with your database, frameworks like Flask, and Django come in handy. There are very less learning curves for them and very useful if you want to handle your ETL jobs 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.
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.
So, let’s explore how Python is used for 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, Python for Data Engineering provides a pySpark interface that 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. So, Python for Data Engineering becomes a common language to effectively communicate between different teams.
4) Data Surfacing
Various data surface approaches exist, including the provision of data into a dashboard or conventional report, or the opening of data simply as a service. Python for Data Engineering is required for setting up APIs to surface the data or models, with frameworks such as Flask, Django.
Let’s check Top python libraries for data engineering
Conclusion
In this article, you learned about the significance of Python for Data Engineering as well as the crucial role played by it. This article also highlighted how python is and the top libraries used in Data Engineering. You also explored various benefits and use cases of Python for Data Engineering.
Overall, Python for Data Engineering is an important concept that plays a pivotal role in any organization.