Data Engineering 102: Introduction to Python for Data Engineering
Tags: datascience, codenewbie, python
Published: 2022-09-04 20:01:30

According to respondents in 2021 in a stack overflow developer’s survey, Python consistently ranked at the top as the most wanted and third most loved programming language. This is because Python is easy to use and has a high level of readability.

Python is a useful tool for performing a data engineer’s job, which is to set up data pipelines and ETL jobs in order to retrieve data from different sources (ingestion), process/aggregate them (transformation), and finally render them available for users, typically business analysts, data scientists, and machine learning experts.

Below are a few use cases of python by data engineers:

**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, 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.

Some of the Python libraries used by data engineers that make data processing, even of large data sets, relatively easy are:

**NumPy:** NumPy is a powerful library for numerical computing in Python. It provides efficient data structures and algorithms for numerical analysis and scientific computing.
Pandas: pandas is a library for data analysis and manipulation. It provides high-performance data structures and functions for working with tabular data.

**Seaborn:** Seaborn is a library for statistical data visualization. It provides functions for plotting data in a variety of statistical formats.
Beautiful Soup: Beautiful Soup is a prominent online scraping and parsing tool on the data extraction front. It provides Python for Data Engineering tools to parse hierarchical information formats, including on the web, for example, HTML pages or JSON files.

**SciPy:** The SciPy module offers a large array of numerical and scientific methods used in Python for Data Engineering that are used by an engineer to carry out computations and solve problems.

Cloud platform providers such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure have all accommodated python users in their solutions to some of the problems since Python has proven itself to be good enough for implementing and controlling their services.

There are many use cases of Python in data engineering, and the language is an indispensable tool for any data engineer.