Python for Data Engineering: An Essential Guide

Data engineering is a complex field that requires a combination of technical and analytical skills to produce effective solutions. The daily operations performed by data engineers involve creating data pipelines that systematically process information to produce actionable insights.

However, performing data engineering tasks is not as easy as it might seem. It involves designing, maintaining, and orchestrating data workflows for an organization. Efficiently performing these operations requires attention to detail, expertise in specialized data engineering tools, and an understanding of a dynamic programming language like Python.

This article will highlight the key factors that indicate the use of Python for data engineering and the libraries that can help simplify complex tasks.

5 Ways Python Is Being Leveraged in Data Engineering

Python is a versatile and robust programming language that is prominently used in data engineering operations. Data engineering primarily focuses on designing, building, and managing data infrastructure with key objectives. These objectives include efficiently extracting data from different sources, transforming it into an analysis-ready format, and loading it into a destination system. Python plays an essential role in this ecosystem by enabling you to process and move complex data through various stages.

Let’s explore the crucial ways in which Python is being leveraged in data engineering.

Data Wrangling with Python

Data wrangling is the process of gathering and transforming raw data and organizing it into a suitable format for analysis. Python, with its powerful libraries like Pandas, NumPy, and Matplotlib, simplifies various tasks involved in data wrangling. By leveraging these libraries, you can enhance data quality and reliability, allowing better interpretation of data.

Python for Data Acquisition

Python can help you quickly gather data from multiple sources. With the help of Python data connectivity libraries, you can effortlessly connect with prominent databases, data warehouses, and data lakes. For example, you can install and import Python-supported connection libraries like PyMSQL and PyMongo.

You can also follow an alternative method to simplify the data acquisition process by using SaaS-based, no-code data engineering tools like Airbyte.

Python Data Structures

Understanding the format of your data is crucial for selecting the most appropriate data structure. Python data structures enable effective data storage. Some of the built-in data structures include lists, sets, tuples, and dictionaries.

For example, JSON data, which is organized as key-value pairs, is suited for storage in Python dictionaries. By supporting special data structures like Pandas DataFrame, Python enables you to manage and analyze tabular data.

Data Storage & Retrieval

Python supports a wide range of libraries for retrieving data in different formats from multiple sources, such as SQL, NoSQL databases, and cloud-based services. The extensive set of resources available in Python makes it a powerful programming language for building data pipelines across different-scale organizations.

For instance, the PyAirbyte library can be used to extract and load data in a Python environment using Airbyte connectors. Here’s a step-by-step guide to extracting data using the Airbyte Python library:

• Create a virtual environment and install PyAirbyte on a local machine. Use Google Colab or any code editor and execute the given code:

• Import PyAirbyte and check if your connector is present in the list of Airbyte connectors by running the code below:

• To install the source in the local environment, execute the following command, replacing the “source-faker” placeholder with a specific connector name.

• You can now configure the source by executing the code below.

• After configuring the source, select all the source streams and use the read method to extract the data from the source.

• These streams can be converted into Pandas DataFrame objects and displayed as tables. You can also modify this data into a different format by applying custom transformations.

Output:‍

• Use libraries like Matplotlib to visualize this data and produce appealing diagrams highlighting the trends in the data.

Output:‍

Using libraries like PyAirbyte, Pandas, and Matplotlib, you can successfully extract, load, transform, and visualize data. To learn more about this tutorial, follow this Google Colab notebook.

Machine Learning

Python is used in almost every machine learning task, whether it is data processing, model selection, training, or evaluation. With libraries like Scikit-learn supporting most machine learning algorithms, it becomes easier to build models in a single environment without performing any migration.

The deep learning libraries, including TensorFlow and PyTorch, enable you to build neural networks for use cases like speech recognition and natural language processing. Python also supports the transformer library provided by HuggingFace, which can be utilized to build large language models.

Must Use Python Libraries for Data Engineering

Most data engineers utilize Python libraries as a part of their daily workflow. Let’s explore a few of them:

PyAirbyte

Utilizing PyAirbyte, you can extract data from multiple sources and load it into prominently used SQL caches, such as DuckDB, Postgres, BigQuery, and Snowflake. The caches are compatible with Python libraries like Pandas, SQL-based tools, and AI frameworks like LangChain. By storing the caches in Pandas, you can perform data transformation operations to either produce insights or make the data compatible with a destination platform.

For example, using PyAirbyte, you can extract product data from a Shopify store, perform a series of transformations, and analyze it to derive meaningful insights. Follow this tutorial to learn more about the Shopify data analysis with PyAirbyte.

Pandas

Pandas, a robust Python library, provides a table-like structure called a DataFrame for handling complex data. Using Pandas, you can easily read and manipulate data in various formats. It offers functionality for tasks such as data cleaning, transformation, and analysis, making it a crucial component of almost every data engineering workflow. Some basic operations that you can perform on Pandas DataFrame include sorting, filtering, and handling missing values.

PyParsing

PyParsing is an alternative to the regular expressions (ReGex) library that eliminates the requirement of manual data parsing techniques. This library enables you to quickly build grammatical understanding within your Python environment using a combination of classes.

For example, to parse the “Hello, World!” text, you can consider running the code below:

Output:‍

Hello, World! -> [‘Hello’, ‘,’, ‘World’, ‘!’]

Apache Airflow

Apache Airflow is an open-source data orchestration tool that you can use to define, monitor, and schedule operations like automated data transformation. It offers a Python library, apache-airflow, that enables you to execute complex data processing pipelines programmatically.

Utilizing Airflow, you can manage workflows as Directed Acyclic Graphs (DAGs). DAGs represent a collection of tasks organized in an order reflecting their relationships.

TensorFlow

TensorFlow is a popular open-source deep learning Python library that helps you create and train custom neural networks. Its capabilities allow you to effectively manage enterprise-scale data processing and modeling operations, including tasks like data preprocessing, transformation, analytics, and visualization.

Key use cases of TensorFlow include analyzing relational data using graph neural networks, building recommendation systems, and more.

Scikit-Learn

scikit-learn is a widely used machine learning library that offers a range of algorithms and functionalities to work with raw data. Built on libraries like NumPy, SciPy, and Matplotlib, it is useful for tasks such as regression, classification, feature extraction, and clustering.

Algorithms like linear regression, logistic regression, decision tree, and random forest are usually used in data science and engineering tasks. This makes scikit-learn the most important tool for developing ML models.

Beautiful Soup

Beautiful Soup is a Python library designed for web scraping and data extraction. It is primarily used to parse HTML and XML content from web pages. By extracting specific text, images, videos, and metadata from websites, you can conduct sentiment analysis or extract insights from product feedback.

Transformers

Hugging Face Transformer is an open-source machine learning library that offers a pre-trained model to perform natural language processing (NLP), computer vision, and multimodal tasks. It supports diverse NLP tasks, such as language modeling, translation, and summarization, and more. The free resources offered by Transformers are being utilized by a range of professionals, including data engineers, researchers, and students.

Python for Data Engineering Use Cases

There are multiple benefits of using Python for data engineering. This section will highlight the key use cases of utilizing Python for performing data engineering tasks.

Large-Scale Data Processing

Python is a crucial tool for processing large-scale data. Compared to other programming languages, it provides a simplistic syntax and extensive library that make it effective to handle scalability with ease. This is the reason why Python is considered in the development and management of data pipelines and machine learning workflows.

Real-Time Data Processing

Utilizing Python's capabilities, you can perform real-time data processing operations. It provides stream processing libraries like Faust and PyFlink, which can help you ingest, filter, modify, and analyze data instantly. Stream processing allows you to generate real-time insights, which are useful in applications like marketing, sensors, and banking applications.

Testing Data Pipelines

Before deploying your data pipeline in a production environment, it is necessary to check if the code performs according to expectations. Python’s testing libraries, such as unittest and pytest can be beneficial in identifying any bugs or issues in the code. This testing ensures that the pipeline doesn’t encounter any issues while deploying.

ETL Automation

You can create scripts that can automate your Python ETL workflows, allowing cost-effective data flow. By automating tasks such as data acquisition, cleaning, transformation, and loading, Python helps streamline most data engineering processes.

How Airbyte Helps Simplify Data Engineering Tasks

Airbyte is a data replication tool that enables you to migrate data from multiple sources to a destination of your preference. Its 400+ pre-built connectors allow you to extract structured, semi-structured, and unstructured data from numerous sources. If the connector you seek is unavailable, you can create custom connectors using Airbyte’s no-code Connector Builder or Connector Development Kits (CDKs).

Here are a few features offered by Airbyte that help you streamline data engineering workflows:

• AI-enabled Connector Builder: The Connector Builder's AI-assist functionality reads through your preferred platform’s API documentation and automatically fills most configuration fields in the user interface.‍
• Support for Vector Databases: Airbyte supports popular vector databases, including Pinecone, Milvus, Weaviate, Qdrant, and Chroma. You can store vector embeddings into these databases, which can further be used to train custom large language models (LLMs).‍
• Streamline GenAI Workflows: With Airbyte, you can convert raw data into vector embeddings using the built-in support for RAG techniques like chunking, embedding, and indexing. Storing these vectors in vector databases enables you to streamline the development of AI applications.‍
• Enterprise-Level Support: Airbyte’s self-managed enterprise edition provides you the capability to manage large datasets in your own virtual private cloud (VPC). With features like role-based access control, personally identifiable information masking, and enterprise support with SLAs, this version provides flexibility and control over your data.

Conclusion

Using Python for data engineering tasks is an efficient way to perform complex data operations and automate workflows. Python’s extensive library allows you to develop custom data pipelines for data migration tasks. Additionally, Python’s transformation capabilities make it an essential part of the ETL workflow. With libraries like Pandas and NumPy, you can modify the source data to make it compatible with the destination.

Beyond data processing, Python’s versatility extends to machine learning, allowing you to build predictive models that can uncover trends and insights from your data. By implementing proper strategies using these insights, you can improve business processes.