Python for Data Engineering

Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation(Sourced from Wikipidea)

Python’s simple, easy-to-learn and readable syntax makes it easy to understand and helps you write short-line codes. Python uses all the attributes of Python and harmonies it for all your Data Engineering needs. Data engineering is increasingly becoming the backbone of companies looking forward to leveraging data to improve business processes. As demand for data engineers is on a rise, and the default programming language for completing various data engineering tasks is approved to be Python (it’s indispensable in data engineering.)

Python for Data Engineering is one of the key skills required in this field to create Data Pipelines. One of the main reasons for this popular accreditation is that it is one of the most popular languages for data science. Python libraries like Pandas, NLTK, scikit-learn, matplotlib

Uses of Python in DE:

1. Data Ingestion- Python helps you get data from various sources

2. Data Acquization- Sourcing data from APIs or through Web Crawlers

3. Data manipulation- Pandas for small datasets and pySpark for large datasets

4. Data modelling- For Machine Learning or deep learning jobs using libraries like Tensorflow/Keras, Scikit-learn, Pytorch

5. Data Surfacing- Provision of data into a dashboard or conventional report

Companies all over the world use Python for their data to obtain insights and a competitive edge. Below snapshot shows the top Python Libraries for Data Engineering

Once you have mastered the Python basics you can follow this learning path to become a Data Engineer