Why Python is top choice for Data Engineering

Python is one of the most popular programming language. Cloud, Big data and Machine Learning have made it very popular in the field of data engineering. I began my Python journey when I started working with AWS. Learning Python has been very rewarding for my professional career. I use Python for developing ETL/Data Pipelines, Modeling, Scheduling, productivity enablers, and interact with various Cloud services. There is not a single day when I don't use Python. Today, I am going to share some of the key reasons for why Python is so much loved by Data Engineers and Data Scientists.

First, Python is easy to learn compared to other programming language. I had done FORTRAN and C programming during my college days.When I started learning Python, I felt it is easier to learn. It has easy syntax and with less coding you can achieve more.If you are from Data warehousing background ( focused on SQL, and ETL Tools) and you want to learn a programming language , Python is the answer. data structures like List, and Dictionary are heavily used in data engineering. Its easy to learn about these data structures in Python and use on daily basis. Nowadays, there are many IDE available which also makes coding much easier.

Second is community Support, Python is open source and has vast community support . There are many modules/libraries available to perform complex tasks easily. Just install these libraries and call the methods/functions by passing few parameters. Data Engineers need to connect to many databases and heterogeneous source systems to integrate data. In such situation, Python libraries come very handy. You don't have to reinvent the wheel, use ready made libraries .This lets you focus on your application development and functionality. Some of the libraries to name are: DB2, snowflake, Oracle ,and Teradata connectors; Pandas, sys,time,matplotlib etc

SDKs: Cloud technologies are getting very popular. Every cloud provider provides SDK for Python. That makes easy to interact with cloud services. Those SDKs have built in methods and just by passing few parameters you can achieve desired functionality. Some of the popular Python SDKs are AWS's boto, GCP's Python APIs.I use python SDKs on daily basis, and I can say that they are cool tool to interact with the cloud services.

Big data APIs: Big data frameworks are so popular for data streaming, data transformation , Analytics, and reporting. Almost all big data frameworks have python APIs. You can write code using these APIs and unleash power of the big data. For example, Spark's Python API, Pyspark is very popular among data engineers.Though you can use some of those frameworks without knowledge of any programming language, you will face many challenges and difficulties. I personally love to use Pyspark for ETL processing.

Frameworks: There are many Python frameworks available which make our job very easy.For Example, if you need to use some web/API development to interact with your database, frameworks like Flask, Django comes handy.There are very less learning curve for them and very useful like if you want to handle your ETL jobs metadata management through web applications.I have used Django many times and its cool.

Dynamic: This is what I like most about programming languages. They give you ability to make things dynamic at run time.SQL/ETL tools have many limitation and making things dynamic at run time is very difficult with them. But using programming language , you can make your code dynamic and change code behavior during/at execution .When you work with data, many time you need power of dynamism and then programming language like Python is there for rescue. I use Python many times to manipulate code at run time, performance tuning , implement CDC, conditioning in flow, ETL pipelines, define dependencies and many more things.

One more thing I do a lot using Python is that development of automated tools or productivity enablers for Design, Modeling, Testing, and Coding. These tools make our job very easy and in less time we can achieve more. This helps to reduce design, development costs and improve quality.

It will become a book if I start writing down that what all can we archive using python.So, I will stop here. What is your take on python? What you like most about Python? Share here in the comment section. Happy Python learning and coding :-)