Why Do Data Engineers Use Python?

Python is one of the most loved programming languages used extensively by web developers and data scientists. Termed often as the language of data, it makes the tasks of data engineers, data analysts and scientists easier. With its robust features and versatility, the language has become indispensable for any data engineer. No wonder, data engineers providing expert data engineering services state that they can’t do their work without Python that makes it one of the most favourite.

With the increased data volume and their significant importance, python is the most endorsed programming language utilized for creating endless applications in several fields. Python is a crucial skill for data engineering as it is used for creating data pipelines, setting up statistical models, and executing a thorough inspection on them.

Check out a few uses of Python language in data engineering:

  1. Data Engineering In the Cloud

Python is an excellent language suited for cloud platform providers. It supports serverless computing that helps data engineers perform ETL on-demand in the cloud, without the need to maintain and pay for a server. Users have the access to physical processing infrastructure to optimize the cost and lessen the management task. Python helps data engineers build data pipelines, and carry out ETL related tasks to recover data from varied sources, operate upon them, and then ultimately make them available to its users.

  1. Data Ingestion

Data ingestion is the access, use and analysis of data transported from varied sources to storage place. Python with its rich set of modules and libraries is used for retrieving the data. Few libraries used for data accessing is SQLAlchemy Beautiful Soup, Scrapy, or Requests for data with web origins. Pandas is a fascinating library that empowers you to study and understand the data from numerous formats like JSON, HTML, XML, CSVs, TSVs. It is an optimized Python package that facilitates a great programming interface with functions required to process and modify data for business utilization.

  1. Parallel Computing

Data Engineers use Apache Spark- an open-source, engine that helps in the big data processing. It has an interface called PySpark that helps you to write Spark apps using Python APIs and to examine the data in a distributed environment using PySpark shell.

PySpark upholds almost all Spark’s features that assist in the effortless development of ETL (Extract, Transform, and Load). Panda’s adepts providing data engineering services use this tool to transform and accumulate a voluminous amount of data and prepare it for end-users. 

  1. Job Scheduling

Written in Python, Apache Airflow is an open-source, workflow control platform. It empowers data engineers to organize and schedule workflow processing cycles and to observe them with the Airflow interface. It allows you to effortlessly visualize the data pipelines, logs, progress, trigger tasks, code, and success status. Thus data engineers use this powerful platform to organize workflows and pipelines.

With its powerful tools, extensive library and rich community support, Python enables data engineers to perform most of their tasks in a time-savvy and seamless manner. Expert data engineers use Python for coding an ETL framework built upon Apache Airflow that is built in Python.