Python is a high-level, interpreted, general-purpose programming language designed by Guido van Rossum in 1991.
Python is Dynamically-typed and garbage-collected. Garbage collection means gaining memory back that has been allocated and is not currently in use in any part of the program.
Python also supports multiple programming paradigms including structural, object-oriented, and functional.
Features of Python
Simplicity: Python Syntax is straightforward, easy to read, and code.
Portability: Python code written on Windows machines can run on other platforms such as Unix and Linux systems, and Mac too.
Easy to Debug: Simply by glancing at the code, you can determine where an error is.
High-Level Language: python does not focus on System architecture or memory management.
Object-Oriented: Python supports Object-oriented language and the concept of classes, objects, inheritance, and encapsulation among others.
large Standard library: Python has a huge standard library that provides modules and functions so that you do not have to write your code for every single thing.
Applications of Python
Data Science, Data Engineering, exploration, and Visualization.
Operating Systems Development
Download the latest version of Python for your operating system from Python Official Website. For Windows System users you can read more about setting up a python development on windows-10 from this article by Digitalocean.com.
If you are using Mac, you can use brew and with an Ubuntu-based desktop, we would recommend using snap.
To learn more about getting started with python basics, you can visit Python Official documentation for more, w3Schools or this blog among others that help beginners learn.
If you are setting up an environment for Data Science or Data Engineering, it is straightforward to get started using Anaconda.
Data Engineering is the art of building/architecting data platforms, designing and implementing data stores and repositories, data lakes and gathering, importing, cleaning, pre-processing, querying, analyzing data, performance monitoring, evaluation, optimization, and fine-tuning the processes and systems.
Critical Aspects of Data Engineering using Python
Now that you got a brief understanding of Python and Data Engineering, we can mention some critical aspects that highlight why Python is essential in Data Engineering. Python for Data Engineering mainly comprises Data Wrangling such as reshaping, aggregating, joining sources of different formats, small-scale ETL, API interaction, and automation.
Python is Popular: Its ubiquity is one of the greatest advantages. In November 2020 it ranked second in the TIOBE Community Index and third in the 2020 Developer Survey of Stack Overflow.
Machine Learning and AI teams also use Python widely: ML, AI, and Data Engineering work closely and have to communicate the same language, Python is the most common one.
large Standard library: A library is a collection of packages and a Package is a collection of modules. Because of Python's
ease of use and various libraries for accessing and manipulating data and databases, 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 is also used in technologies such as Apache Airflow and libraries for popular tools such as Apache Spark. If you intend to use these tools, it is important to know the Language you utilize.
Common Python Packages used in Data Engineering
Pandas is a Python Open-Source Package for manipulating and processing data frames. Pandas handle, read, aggregate, filter, reshape, and export data into various formats quickly and easily.
This is a module for Scientific Computing with Python. Data Engineers rely on it in carrying out computations and solving problems.
Beautiful Soup is a library for web scraping and data mining. It provides Data Engineers with a tool to extract data from websites such as HTML pages and JSON files.
Is a Python Framework that provides commonly used functionality for the development of Extract-Transform-Load (ETL) processes due to its efficiency.
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 few lines of code, in addition to supporting data imports from CSV, JSON, and SQL.
Advantages of using Python for Data Engineering over Java
Ease of use: Although both Python and Java are expressive, Python is more user-friendly and concise. Python helps you write short-line codes compared to Java.
Wide range of Applications: Python is used in Data Science, Big Data, Data mining, Artificial Intelligence, and Machine Learning. This enables Python to be more preferred in Data Engineering than Java.
Use Cases of Python for Data Engineering
Data Acquisition: Involves acquiring Data from APIs or through web scraping using python. ETL jobs require Python skills to use platforms such as Airflow.
PyMoDAQ, An open-source Python-based tool is used for modular data acquisition.
Data Manipulation: Python for Data Engineering provides a PySpark interface that allows manipulation on large Datasets using Spark clusters. Pandas on the other hand can be used to manipulate small datasets.
Data Modelling: Python is a common language to use when working with teams undertaking Machine Learning, using frameworks such as Tensorflow and Pytorch.
In conclusion, Python is a key Language for Data Engineers and for those who are aspiring to become Data Engineers too. Data Engineers use Python and Python Libraries, packages, and modules in their daily routines to wrangle Data and Create Data Pipelines
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