Introduction To Python for Data Engineering

The rate at which data has continually been generated in the world is fast rising. Seagate UK has predicted that by 2025 there will be 175 zettabytes of data in the global data-sphere. From the rapid daily data growth, data engineering as a subdiscipline that focuses directly on transportation, transformation, and storage of data has steadily established itself in the technology realm.
Data Engineering has a deeper focus on the industrial process of data such as data pipelines and ETL (extract, transform, load) jobs. Data engineers are responsible for the provision of quality data for analysis and data-driven decision-making. For data engineers to successfully achieve their objectives, Python is primarily employed. Python Programming language is an interpreted general-purpose high-level programming language that emphasizes code readability and significant use of indentation
Why Python?
Over the years, other technologies or programming languages have been used in data handling and manipulation. However, python has since trampled over these technologies to become the most popular programming language worldwide. Its popularity and preference mainly stem from:
Ease of use: python is simple, its ease to learn and read syntax makes it easy to understand and helps one writes shortlines. Its ease of use enhances its user-friendliness hence garnering higher preference.
Learning Curve. Python provides a simple to grasp learning curve while also offering good standard libraries that have concise syntax.
Wide applications. Providing a wide scope of libraries and packages, python enhances the possibility to complete many tasks across different domains efficiently and effectively.
Python for Data Engineers
Data Engineers are responsible for preparing and polishing the data so that it can be used for various tasks related to prediction, analytics, etc. python is used by data engineers for;
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.
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 the manipulation of large datasets using Spark clusters.
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.
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, and Django.
There are many use cases of Python in data engineering, and a language is an indispensable tool for any data engineer.