Introduction to Python for Data Engineering
Published: 2022-09-29 06:23:21

Demand, storage and usage of data is increasingly becoming more of a “must” rather than “if”. It is estimated that “By** 2025**, there will be **175 zettabytes** of data in the global data-sphere”. 

**Companies** are now placing a higher value on data. Companies are discovering new ways to use data to their advantage. Data can and is being used to analyze the current status of their business, forecast the future, model their customers, avoid threats and develop new goods. Data Engineering is the linchpin in all these activities.

As** Data Engineering **lies at the core of handling and processing data, the second question that begs to be asked is **“What tools/technologies can be leveraged to derive maximum benefit from data with minimum and least complicated effort.” ****Python** here presents itself as an ideal candidate, Python is **today’s most popular programming language** with endless applications in various fields. It is ideally suited for deployment, analysis, and maintenance thanks to its **flexible and dynamic nature.** Thus here the concept “Python for Data Engineering” is introduced as one of the most crucial skills required in data engineering: to create Data Pipelines, set up Statistical Models, and perform a thorough analysis on them.

**Python for Data Engineering** mainly comprises **Data Wrangling such as reshaping, aggregating, joining disparate sources, small-scale ETL(Extract, Transform ,Load), API interaction, and automation.**

For numerous reasons, Python is popular. Its ubiquity is one of the greatest advantages. Python is one of the world’s three leading programming languages. For instance, in November 2020 it ranked second in the TIOBE Community Index and third in the 2020 Developer Survey of Stack Overflow.
Python is a general-purpose, programming language. Because of its ease of use and various libraries for accessing databases(Boto3, Psycopg2, mysql connectors) 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.
Machine Learning and AI teams also use Python widely. Teams working together closely, typically have to communicate in the same language, while Python is the lingua franca in the field.
Another reason Python is more popular is its use in technologies such as Apache Airflow and libraries for popular tools such as Apache Spark. If you have tools like these in your business, it is important to know the languages you utilize.
Python Developer Community- There exists a very wide and rich python community that offers solutions and support for bugs that you might encounter .
Python for Data Engineering is popular rather than Java. Python has a broad range of characteristics that distinguish it from other languages of programming. Some of those features are given below:

Ease-of-Use: Both are expressive and we can achieve a high functionality level with them. Python is more user-friendly and concise. Python’s simple, easy-to-learn and read syntax makes it easy to understand and helps you write short-line codes as compared to Java.
Learning Curve: In addition to having support communities, they are both functional and object-oriented languages. Because of its high-level functional characteristics, Java is a bit more complex than Python to master. For simple intuitive logic, Python is preferable, whereas Java is better used in complex workflows. Concise syntax and good standard libraries are provided by Python.
Wide Applications: The biggest benefit of Python over Java is the simplicity of use in Data Science, Big Data, Data Mining, Artificial Intelligence, and Machine Learning

**Top 5 Python Packages used in Data Engineering:**

**1. Pandas**
Pandas is a Python open-source package that offers high-performance, simple-to-use data structures and tools to analyze data. Pandas is the ideal Python for Data Engineering tool to wrangle or manipulate data. It is meant to handle, read, aggregate, and visualize data quickly and easily.

**2. pygrametl**

pygrametl delivers commonly used programmatic ETL development functionalities and allows the user to rapidly build effective, fully programmable ETL flows.

**3) petl**

petl is a Python library for the broad purpose of extracting, manipulating, and loading data tables. It offers a broad range of functions to convert tables with little lines of code, in addition to supporting data imports from CSV, JSON, and SQL.

**4) 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.

**5) 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.