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Neelam

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Use Cases of Python for Data Engineering

Nowadays, data is essential for every business. Companies use data to answer business queries such as what's most valuable to a potential customer, how do I improve my website's functionality, or find the fastest-growing product.
Businesses of all sizes can integrate diverse data to address critical business questions. This process is supported through Data Engineering, which provides data consumers like Data Analysts, Data Researchers, and Managers to access an efficient, secure, quick, and thorough analysis of all the available data. Let's look at how companies utilize Python to perform Data Engineering:

1.) Data Acquisition
Finding data through APIs or Web Crawlers involves the use of Python. Additionally, the scheduling and orchestrating of ETL tasks using platforms, such as Airflow or Airflow, requires Python abilities.

2.) Data Manipulation
Python libraries like Pandas permit the manipulation of small data sets. Additionally, Python for Data Engineering offers a park interface that permits manipulating large datasets with Spark clusters.

3.)Data Modelling
Python is used to run Machine Learning or Deep Learning tasks, using frameworks such as Scikit-learn, Tensorflow/Keras, and Pytorch. This makes it an efficient language that can connect teams.

4.) Data Surfacing
There are a variety of data surface methods available that allow the integration of data in an existing report or dashboard and the access of data to be used as an application. Python is a data engineering language. Data Engineering is required to create APIs to display the models or data using frameworks like Flask, Django.
These cases show the significance of Python to Data Engineering in the present day.

Conclusion
In this article, you will learn about the importance and importance of Python in Data Engineering and its vital role in the field. This article also discussed the five most popular Python applications employed for Data Engineering. The article also discussed the various advantages and applications that use Python in Data Engineering. In the end, Python for Data Science is a crucial concept that plays an essential part in any company.
Therefore, as long as there's data to process, Data engineers will always be needed. Data Insights revealed in 2019 that Data Engineering is a top sought-after job in the technology sector and is ahead of Computer Scientists, Web Designers, and Database Architects. Additionally, LinkedIn listed the job as one of its expected to grow in 2021.

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