The rate of data generation has increased throughout this century at a predictable rate more or less. According to Seagate UK, “By 2025, there will be 175 zettabytes of data in the global data-sphere”. Companies place a higher value on data. Companies are discovering new ways to use data to their advantage. They use data to analyze the current status of their business, forecast the future, model their customers, avoid threats and develop new goods. Data Engineering is the linchpin in all these activities.
Python is today’s most popular programming language with endless applications in various fields. It is ideally suited for deployment, analysis, and maintenance thanks to its flexible and dynamic nature. Python for Data Engineering is one of the crucial skills required in this field to create Data Pipelines, set up Statistical Models, and perform a thorough analysis on them.
This article will dive deep into the importance of Python for Data Engineering and the role played by Python in this field. Moreover, you will get to know more about the top 5 python packages used and a few use cases of Data Engineering using Python. So, read along to gain more insights into the role of Python for Data Engineering.
Table of Contents
- Introduction to Python
- Introduction to Data Engineering
- Significance of Python for Data Engineering
- Critical Aspects of Data Engineering using Python
- Pros of Data Engineering using Python over Java
- Top 5 Python Packages used in Data Engineering
- Use Cases of Python for Data Engineering
Introduction to Python
Python is one of the most popular programming languages. It is an open-source, high-level, object-oriented programming language created by Guido van Rossum. Python’s simple, easy-to-learn and readable syntax makes it easy to understand and helps you write short-line codes. In addition to this, Python has an ocean of libraries that serve a plethora of use cases in the field of Data Engineering, Data Science, Artificial Intelligence, and many more. Some popular examples include Pandas, NumPy, SciPy, among many others.
Python lets you work quickly and integrate systems more efficiently. It has a huge robust global community with many tech giants like Google, Facebook, Netflix, IBM having dependencies on it. Python allows interactive testing and debugging of code snippets and provides interfaces to all major commercial databases. Python for Data Engineering uses all the features of Python and fine-tunes it for all your Data Engineering needs.
To read more about Python, click here.
Introduction to Data Engineering
Data Engineering is becoming popular with the large volume, variety, and velocity of technology changes. The phrase “Data Engineer” came into being around 2011, in the circles of emerging data-driven organizations such as Facebook and Airbnb. Data Engineering has grown to reflect a role that has moved away from standard ETL tools and has built its tools for managing rising data volumes. With Big Data growing, Data Engineering describes a sort of Software Engineering, which focuses on data – Data Infrastructure, Data Warehousing, Data Mining, Data Modelling, Data Crunching, and Metadata Management.
Data Engineering aims ultimately at providing ordered, consistent data flow to permit the processing of data such as:
- Training Machine Learning models
- Perform Exploratory Data Analysis
- Populate fields with External Data in an application
It is imperative nowadays that enterprises require abundant Data Engineers to provide the foundations for effective Data Science projects in the context of full digital corporate transformations, the Internet of Things, and the race to become AI-drifty. Data Engineers create and build pipelines for the transformation and transfer of information in such a way that it is beneficial for Data Scientists, Data Analysts, or other end-users. Briefly, a Data Engineer is in charge of managing a large number of data and sending this data into Data Science Pipelines.
Python for Data Engineering uses all the concepts of Data Engineering and applies that to a versatile language like Python.
Significance of Python for Data Engineering
Now that you got a brief overview of both Python and Data Engineering, let’s discuss the significance of Python for Data Engineering is important. Key programming abilities are necessary for a general understanding of Data Engineering and Pipelines. For Data Analysis and Pipelines, Python is primarily employed. Python is a general-purpose programming language that is becoming ever more popular for Data Engineering. Companies all over the world use Python for their data to obtain insights and a competitive edge.
Sitting on mountains of potentially lucrative real-time data, these organizations required Software Engineers to design tools for handling all the data rapidly and efficiently. In order to work with data, Data Engineers utilize specialized tools. The way data is modeled, stored, safeguarded, and encoded must be considered. These teams must also know how to access and handle the data efficiently. Hence, knowledge of core programming languages like Python is a must.
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Critical Aspects of Data Engineering using Python
Now that you have got a brief understanding of Python and Data Engineering, this section mentions some critical aspects that highlight the role of Python for Data Engineering. Python for Data Engineering mainly comprises Data Wrangling such as reshaping, aggregating, joining disparate sources, small-scale ETL, API interaction, and automation.
- For numerous reasons, Python is popular. Its ubiquity is one of the greatest advantages. Python is one of the world’s three leading programming languages. For instance, in November 2020 it ranked second in the TIOBE Community Index and third in the 2020 Developer Survey of Stack Overflow.
- Python is a general-purpose, programming language. Because of its ease of use and various libraries for accessing databases and storage technologies, it has become a popular tool to execute ETL jobs. Many teams use Python for Data Engineering rather than an ETL tool because it is more versatile and powerful for these activities.
- Machine Learning and AI teams also use Python widely. Teams working together closely, typically have to communicate in the same language, while Python is the lingua franca in the field.
- Another reason Python is more popular is its use in technologies such as Apache Airflow and libraries for popular tools such as Apache Spark. If you have tools like these in your business, it is important to know the languages you utilize.
These are just a few reasons how important the role of Python for Data Engineering is in today’s world.
Pros of Data Engineering using Python over Java
In this section, you will explore the various benefits of Data Engineering using Python over Java. These are some of the reasons Python for Data Engineering is popular rather than Java. Python has a broad range of characteristics that distinguish it from other languages of programming. Some of those features are given below:
- Ease-of-Use: Both are expressive and we can achieve a high functionality level with them. Python is more user-friendly and concise. Python’s simple, easy-to-learn and read syntax makes it easy to understand and helps you write short-line codes as compared to Java.
- Learning Curve: In addition to having support communities, they are both functional and object-oriented languages. Because of its high-level functional characteristics, Java is a bit more complex than Python to master. For simple intuitive logic, Python is preferable, whereas Java is better used in complex workflows. Concise syntax and good standard libraries are provided by Python.
- Wide Applications: The biggest benefit of Python over Java is the simplicity of use in Data Science, Big Data, Data Mining, Artificial Intelligence, and Machine Learning.
Top 5 Python Packages used in Data Engineering
Python provides an ample amount of libraries and packages for various applications. In this section, we will discuss the top 5 Python for Data Engineering packages. The top 5 Python packages include:
Pandas is a Python open-source package that offers high-performance, simple-to-use data structures and tools to analyze data. Pandas is the ideal tool to wrangle or manipulate data. It is meant to handle, read, aggregate, and visualize data quickly and easily.
pygrametl delivers commonly used programmatic ETL development functionalities and allows the user to rapidly build effective, fully programmable ETL flows.
petl is a Python library for the broad purpose of extracting, manipulating, and loading data tables. It offers a broad range of functions to convert tables with little lines of code, in addition to supporting data imports from CSV, JSON, and SQL.
4) Beautiful Soup
Beautiful Soup is a prominent online scraping and parsing tool on the data extraction front. It provides tools to parse hierarchical information formats, including on the web, for example, HTML pages or JSON files.
The SciPy module offers a large array of numerical and scientific methods that are used by an engineer to carry out computations and solve problems.
Use Cases of Python for Data Engineering
Today, data is crucial to every company. Companies utilize data to answer business questions like what’s valuable for a new client, how can I enhance my website, or what is the most rapidly expanding products.
Companies of all sizes are able to combine large quantities of heterogeneous data to answer crucial business issues. The process is supported by Data Engineering, which allows data consumers, such as Data Analysts, Data Researchers, and Managers, a secure, reliable, fast, and complete inspection of all available data. So, let’s explore how organizations use Python for Data Engineering:
1) Data Acquisition
Sourcing data from APIs or through Web Crawlers involves the use of Python. Moreover, scheduling and orchestrating ETL jobs using platforms such as Airflow, require Python skills.
2) Data Manipulation
Python libraries such as Pandas allow for the manipulation of small datasets. In addition to this, Python for Data Engineering provides a pySpark interface that allows manipulation on large datasets using Spark clusters.
3) Data Modelling
Python is used for running Machine Learning or Deep Learning jobs, using frameworks like Tensorflow/Keras, Scikit-learn, Pytorch. So, it becomes a common language to effectively communicate between different teams.
4) Data Surfacing
Various data surface approaches exist, including the provision of data into a dashboard or conventional report, or the opening of data simply as a service. Python is required for setting up APIs to surface the data or models, with frameworks such as Flask, Django.
These use cases highlight the importance of Python for Data Engineering in our world.
In this article, you learned about the significance of Python for Data Engineering as well as the crucial role played by it. This article also highlighted the top 5 Python packages used in Data Engineering. You also explored various benefits and use cases of Python for Data Engineering. Overall, Python for Data Engineering is an important concept that plays a pivotal role in any organization.
So, as long as there is data to process, data engineers will be in demand. Dice Insights reported in 2019 that Data Engineering is a top trending job in the technology industry, beating out Computer Scientists, Web Designers, and Database Architects. Moreover, LinkedIn listed it as one of its jobs on the rise in 2021.
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