Python for Data Engineering

Today’s data-driven organizations rely on efficiency in data engineering tasks. As the demand for data increases, teams must have the ability to collect, process, and store extremely large volumes of data, and Python has emerged as a vital asset for accomplishing this mission. Teams use Python for data engineering tasks due to its flexibility, ease of use, and rich ecosystem of libraries and tools. In this article, we’ll delve into the world of data engineering with Python, discuss how it’s being used, and share some of its most popular libraries and use cases for data engineering.

How Python Is Being Used in Data Engineering

With the shift from analytics to machine learning and app development, the logic and transformations of data became more complex and required the flexibility of programming languages such as Python. Python’s inherent characteristics and the wealth of resources that have grown around it have made it the data engineer’s language of choice. Here are a few examples of how modern teams are using Python for data engineering.

Data acquisition

Python is used extensively to gather data relevant to a project. Data engineers use Python libraries to acquire data via web scraping, interacting with the APIs many companies use to make their data available and connecting with databases.

Data wrangling

With libraries for cleaning, transforming, and enriching data, Python helps data engineers create usable, high-quality data sets ready for analysis. Python’s powerful libraries for data sampling and visualization allow data scientists to better understand their data, helping them uncover meaningful relationships in the larger data set.

Custom business logic

Bringing data into dashboards, machine learning models, and applications involves complex data and business logic transformations that require the use of complex business logic defined as code. Because of the simplicity of Python, it is often used to write this logic and execute it as part of a data pipeline or data transformation, triggering actions downstream as part of a business process or an application.

Data storage and retrieval

Python’s libraries offer solutions for accessing data stored in a variety of ways, including in SQL and NoSQL databases and cloud storage services. Thanks to these resources, Python has become critical to building data pipelines. In addition, Python is used to serialize data, making it possible to store and retrieve data more efficiently.

Machine learning

Python is also deeply embedded into the machine learning process, finding applications in nearly every aspect of ML, including data preprocessing, model selection and training, and model evaluation. With applications for deep learning, Python provides a powerful selection of tools for building neural networks and is often used for tasks such as image classification, natural language processing, and speech recognition.

Popular Python Libraries for Data Engineering

One of Python’s primary advantages for data engineering tasks is its extensive ecosystem of libraries. These libraries provide data engineers with a wide range of tools to work with, helping them manipulate, transform, and store data faster and more effectively. From small data projects to large-scale data pipelines, these six popular Python libraries streamline data engineering tasks:

1. Pandas

The pandas library is one of the most frequently used libraries for data engineering in Python. This versatile library equips data engineers with powerful manipulation and analysis capabilities. Pandas is used to preprocess, clean, and transform raw data for downstream analysis or storage.

2. Apache Airflow

Apache Airflow serves as a platform for data engineers to author, schedule, and monitor workflows. It provides an easily accessible, intuitive interface data engineers can use to create, schedule, and execute multiple tasks, as well as manage complex data processing pipelines.

3. Pyparsing

Pyparsing is a Python class library that eliminates the need to manually craft a parsing state machine. Pyparsing allows data engineers to build recursive descent parsers quickly.

4. TensorFlow

TensorFlow is a popular Python library for machine learning and deep learning applications by providing a versatile platform for training and serving models. One of TensorFlow’s primary value adds is its ability to handle large-scale data processing and modeling tasks, including data preprocessing, data transformation, data analytics, and data visualization.

5. Scikit-learn

Built atop libraries, including NumPy and SciPy, the scikit-learn library offers data engineers a broad selection of machine learning algorithms and utilities for working with structured data. Data engineers use scikit-learn for tasks such as data classification, regression, clustering, and feature engineering to streamline the process of building machine learning models and pipelines.

6. Beautiful soup

Beautiful Soup is one of the most effective tools for web scraping and data extraction, making it a valuable asset for data engineering. Using Beautiful Soup, data engineers can easily parse HTML and XML documents, extract specific data from such as websites and web pages—for example, text, images, links, and metadata—and quickly navigate document trees.

Python for Data Engineering Use Cases

Python can be used for myriad data engineering tasks. The following three use cases highlight how today’s teams are using Python to solve real-world data engineering challenges.

Real-time data processing

Python effectively powers stream processing, a data management technique where data is ingested, analyzed, filtered, transformed, and enhanced in real time. Using Python, stream processing enables teams to generate insights from data as it’s being created with direct application to marketing, fraud detection, and cybersecurity use cases.

Large-scale data processing

Python is one of the most popular languages for processing data at scale. Its simplicity, scalability, and efficiency make it ideal for processing massive amounts of data at speed. This is why it’s commonly used for data pipelines and machine learning applications.

Data pipeline automation

By removing manual processes, data pipeline automation facilitates the free flow of data, reducing the time to value. Python’s deep bench of libraries and tools makes it easy to automate the data pipeline process, including data acquisition, cleaning, transformation, and loading.

Streamline Your Python Data Engineering Workflows with Snowflake

Today, Python occupies a prominent place in the data engineering field. Ideal for working with data at scale, this programming language is helping data engineers prepare their data for a number of analytical and operational uses. Snowflake makes it possible to accelerate data engineering workflows when using Python and other popular languages.

With Snowpark, the new developer experience for Snowflake, data engineers can write code in their preferred language and run code directly on Snowflake. Snowpark  accelerates the pace of innovation by leveraging Python’s familiar syntax and thriving ecosystem of open-source libraries to explore and process data where it lives. Build powerful and efficient pipelines, machine learning (ML) workflows, and data applications while enjoying the performance, ease of use, and security of working inside the Snowflake Data Cloud.

See Snowflake’s capabilities for yourself. To give it a test drive, sign up for a free trial.