Introduction to python for data engineering.
Tags: python, programming, beginners, aws
Published: 2022-09-04 17:08:32

What is python programming? Python is an interpreted, object-oriented, high-level programming language with dynamic semantics.
  
Python becoming a popular programming language globally. We commonly knew that python was common for data science, machine learning/deep learning. Surprisingly it is also used for data engineering because it easy and smooth. Using Python Pandas data frames allows data engineers to process data effectively. Additionally, using python programming for data engineering is an excellent approach to understanding the requirements of data scientists better. Python also helps data engineers to build efficient data pipelines as many data engineering tools use Python in the backend. Moreover, various tools in the market are compatible with Python and allow data engineers to integrate them into their everyday tasks by simply learning Python programming language. 

**Advantages of python for data engineering**

1.The role of a data engineer involves working with different types of data formats. For such cases, Python is best suited. Its standard library supports easy handling of .csv files, one of the most common data file formats.

2.A data engineer is often required to use APIs to retrieve data from databases. The data in such cases is usually stored in JSON (JavaScript Object Notation) format, and Python has a library named JSON-JSON to handle such type of data.

3.The responsibility of a data engineer is not only to obtain data from different sources but also to process it. One of the most popular data process engines is Apache Spark which works with Python DataFrames and even offers an API, PySpark, to build scalable big data projects.

4.Data engineering tools use Directed Acyclic Graphs like Apache Airflow, Apache NiFi, etc. DAGs are nothing but Python codes used for specifying tasks. Thus, learning Python will help data engineers use these tools efficiently.

5.Luigi! a Python module that is widely considered a fantastic tool for data engineering.

6.Python is easy to learn and is free to use for the masses. 

**Python libraries used for data engineering**

**pandas** Pandas is the Python library popular among data analysts and data scientists. It is equally useful for data engineers, who often use it for reading, writing, querying, and manipulating data. The advantage of using Pandas dataframes is they are extremely compatible with two popular data types .csv and JSON. Additionally, dataframe objects have many easy-to-use functions that data engineers can perform quick exploratory data analysis. They can also use it to fix common data problems, such as replacing null values with neighborhood averages, removing columns, etc. Thus, Pandas allows data engineers to transform it into a readable and organized form.

**SciPy** SciPy, as the name suggests, is a library in Python that offers various functions for quick mathematical computations. A data engineer can use this library to perform scientific calculations on their data for better analysis.

**Beautifulsoup** This is a well-known library used for data mining and web scraping. You will find data engineers using this to extract information from websites, dealing with JSON/HTML data formats, all for preparing their data.

**Petl** Petl is a Python package for extracting, modifying, and loading tabular data. Data engineers use this library for building ETL (Extract, Transform, and Load) pipelines. 

**Great Expectation** While Pandas is an essential library for analyzing data; there is even a better method to draw relevant conclusions from your data. And that method is to use the Great expectations library. It makes it easy for data engineers to clean data equally and allows them to specify their expectations simply. The library takes care of the backend logic, and it does not matter whether your data belongs to a database or is stored in a dataframe. Additionally, it makes it convenient for data engineers to add production-grade validation to a given data.

**So how do you learn python for data engineering**
Inorder to grasp the concept well and understand you have to work on real world python projects for data engineers.

**Data ingestion** - refers to collecting data from the database for immediate use. A data engineer needs to learn various tools like SQL, Python, etc., to know how to connect to a database and retrieve data. 

**Data Manipulation** - A data engineer deals with data of both types, structured and unstructured. Once they have sourced data from the warehouse, the next step is to implement mathematical operations on it for cleaning.

**Data surfacing** -Data Surfacing involves building insightful dashboards to help businesses make better and quicker decisions. As a data engineer is the one who prepares the input data for such dashboards, it will be beneficial for them if they know how such dashboards are built.

**Data acquisition** - Not always a business is aware of how to identify sources of data. This is where a data engineer comes into the picture, as he is expected to identify the sources, for example, obtaining a website's log data using APIs.

** Data pipeline** - All the steps that a data engineer performs are eventually automated with the help of data pipelines. Depending on the organization's requirements, these pipelines can be of the type ETL/ELT, depending on the organization needs.

**Benefits of python**

1.Reduces development time.
2.Object oriented language.
3.No compiling.
4.Supports dynamic data type.
5.Reduce code length.
6.Easy to use learn and use as developers.
7.Easy to understand codes.
8.Easy to do team projects.
9.Easy to extend to other languages.
10.It's free(open source).

**What is most necessary with Python to become a data engineer?**
To become a data engineer with Python, the most necessary part is to explore as many Big Data projects and tools as possible.

**Conculsion**
Currently data engineering is in demand and not flooded another advantage is that being a data engineer allows you to interact with Big data projects and tools more than data scientists and analysts do. Consistency is key... And you can be anyone you want to be as long as you have a growth mindset.