Why learn Python for Data Engineering?

Python has emerged as one of the most popular programming languages globally. It is a popular choice among data scientists for completing analytics and machine learning/deep learning applications. But, don’t be surprised to note that Python is also becoming popular among data engineers. The reason behind that is data engineering with Python is smooth. One can quickly realize this if one uses Python for a data engineering project.

What are the advantages of using Python for Data Engineering? Data engineering using Python only gets better, and here is a list of points if you are beginning to think otherwise.

• 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.
• 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.
• 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.
• 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.
• Luigi! No, not the Mario character by Nintendo; we are referring to the Python module that is widely considered a fantastic tool for data engineering.
• Apart from all the points mentioned above, it is common knowledge that Python is easy to learn and is free to use for the masses. An active community of developers strongly supports it.

Using Python Pandas data frames allows data engineers to process data effectively. Additionally, using python programming for data engineering is an excellent approach to better understanding data scientists' requirements. 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. Let us now discuss such tools and how they help data engineers in the industry.

Top Python Libraries for Data Engineering One of the most important features of Python that makes it a perfect fit for data engineering applications is the libraries that it has. Let us explore which are libraries and how data engineers use them.

• 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.
• Psycopg2, pyodbc, sqlalchemy When one hears the word ‘database’, they are likely to think of data stored in the form of tables having various rows and columns. Such type of a database is called a relational database. There are several ways of interacting with such databases and most of them are based on Structured Query Language (SQL). One such tool popular among data engineers is MyPostgreSQL, and Python contains various libraries to connect to MyPostgreSQL, including pyodbc, Sqlalchemy, and psycopg2.
• Elasticsearch While relational databases are commonly used in the industry, it is not the only data type. The other types of databases include key-value, columnar, time-series, NoSQL, etc. To handle NoSQL databases (that do not contain data in rows and columns), data engineers usually use Elasticsearch. Python allows users to manage NoSQL databases with its elastic search library.
• Great Expectations 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 data frame.
• 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.
• pygrametl This is another library that supports the efficient deployment of ETL pipelines.

How do I start Learning Data Engineering? Good question! So, one way to learn Python for data engineering is to start reading a book and take your time to absorb it. But, a more fun and exciting way to learn Python for data engineering is to start working on real-world python projects for data engineering. So, check out the list of projects below and get started.

• Data Ingestion 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 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 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. Work on the project idea mentioned below to know more.
• 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. So, here is a project idea to help you with the same. Parallel Computing with PySpark One of the most popular tools for transforming data in streams or batches, is Apache Spark. Python has an API, PySpark, that allows Python users to process large amounts of data. If you are an aspiring data engineer who knows Python, work on the project below to learn how data frame operations smoothen data transformation.
• Data Pipelines 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's needs. Work on the project below to learn how such pipelines can be created with the help of big data tools like SnowFlake, AWS, Apache Airflow, and Kinesis.

With all this knowledge, I actually believe it's essential to keep you in place and in toes in your Data Engineering Journey. We keep on learning and striving to be the better part of ourselves by the day, I hope you stay on track and see you on my next article. Adios Muchacho!