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As a data engineer, the list of tools and frameworks you’re expected to know can often be daunting. But, at the least, you should be proficient in SQL, Python, and Bash scripting.
Beside being familiar with core Python features and built-in modules, you should also be comfortable working with Python libraries for tasks you’ll do all the time as a data engineer. Here, we’ll explore a few such libraries to help you with the following tasks:
- Working with APIs
- Web scraping
- Connecting to databases
- Workflow orchestration
- Batch and stream processing
Let’s get started.
1. Requests
As a data engineer, you’ll often work with APIs to extract data. Requests is a Python library that lets you make HTTP requests from within your Python script. With Requests, you can retrieve data from RESTful APIs, fetch web pages for scraping, send data to server endpoints, and more.
Here’s why Requests is super popular among data professionals and developers alike:
- Requests provides a simple and intuitive API for making HTTP requests, supporting various HTTP methods such as GET, POST, PUT, and DELETE.
- It handles features like authentication, cookies, and sessions.
- It also supports features like SSL verification, timeouts, and connection pooling for robust and efficient communication with web servers.
To get started with Requests, check out the Quickstart page and the Advanced Usage guide in the official docs.
2. BeautifulSoup
As a data professional (whether a data scientist or a data engineer), you should be comfortable with programmatically scraping the web to collect data. BeautifulSoup is one of the most widely used Python libraries for web scraping which you can use for parsing and navigating HTML and XML documents.
Let’s list some of the features of BeautifulSoup that make it a great choice for web scraping tasks:
- BeautifulSoup provides a simple API for parsing HTML documents. You can search, filter, and extract data based on tags, attributes, and content.
- It supports various parsers, including lxml and html5lib—offering performance and compatibility options for different use cases.
From navigating the parse tree to parsing only a part of the document, the docs provide detailed guidelines for all tasks you may need to perform when using BeautifulSoup.
Once you’re comfortable with BeautifulSoup, you can also explore Scrapy for web scraping. For most web scraping tasks, you’ll often use Requests in conjunction with BeautifulSoup or Scrapy.
3. Pandas
As a data engineer, you’ll deal with data manipulation and transformation tasks regularly. Pandas is a popular Python library for data manipulation and analysis. It provides data structures and a suite of functions necessary for cleaning, transforming, and analyzing data efficiently.
Here’s why pandas is popular among data professionals:
- It supports reading and writing data in various formats such as CSV, Excel, SQL databases, and more
- As mentioned, pandas also offers functions for filtering, grouping, merging, and reshaping data.
The Pandas Tutorial: Pandas Full Course by Derek Banas on YouTube is a comprehensive tutorial to become comfortable with pandas. You can also check 7 Steps to Mastering Data Wrangling with Python and Pandas on tips for mastering data manipulation with pandas.
Once you’re comfortable with pandas, depending on the need to scale data processing tasks, you can explore Dask. Which is a flexible parallel computing library in Python, enabling parallel computing on clusters.
4. SQLAlchemy
Working with databases is one of the most common tasks you’ll do in your workday as a data engineer. SQLAlchemy is a SQL toolkit and an Object-Relational Mapping (ORM) library in Python which makes working with databases simple.
Some key features of SQLAlchemy that make it helpful include:
- A powerful ORM layer that allows defining database models as Python classes, with attributes mapping to database columns
- Allows writing and running SQL queries from Python
- Support for multiple database backends, including PostgreSQL, MySQL, and SQLite—providing a consistent API across different databases
You can check the SQLAlchemy docs for detailed reference guides on the ORM and features like connections and schema management.
If, however, you work mostly with PostgreSQL databases, you may want to learn to use Psycopg2, the Postgres adapter for Python. Psycopg2 provides a low-level interface for working with PostgreSQL databases directly from Python code.
5. Airflow
Data engineers frequently deal with workflow orchestration and automation tasks. With Apache Airflow, you can author, schedule, and monitor workflows. So you can use it for coordinating batch processing jobs, orchestrating ETL workflows, or managing dependencies between tasks, and more.
Let’s review some of Airflow’s features:
- With Airflow, you define workflows as DAGs, scheduling tasks, managing dependencies, and monitoring workflow execution.
- It provides a set of operators for interacting with various systems and services, including databases, cloud platforms, and data processing frameworks.
- It is quite extensible; so you can define custom operators and hooks as needed.
Marc Lamberti’s tutorials and courses are great resources to get started with Airflow. While Airflow is widely used, there are several alternatives such as Prefect and Mage that you can explore, too. To learn more about Airflow alternatives for orchestration, read 5 Airflow Alternatives for Data Orchestration.
6. PySpark
As a data engineer, you’ll need to handle big data processing tasks that require distributed computing capabilities. PySpark is the Python API for Apache Spark, a distributed computing framework for processing large-scale data.
Some features of PySpark are as follows:
- It provides APIs for batch processing, machine learning, and graph processing amongst others.
- It offers high-level abstractions like DataFrame and Dataset for working with structured data, along with RDDs for lower-level data manipulation.
The PySpark Tutorial on freeCodeCamp’s community YouTube channel is a good resource to get started with PySpark.
7. Kafka-Python
Kafka is a popular distributed streaming platform, and Kafka-Python is a library for interacting with Kafka from Python. So you can use Kafka-Python when you need to work with real-time data processing and messaging systems.
Some features of Kafka-Python are as follows:
- Provides high-level Producer and Consumer APIs for publishing and consuming messages to and from Kafka topics
- Supports features like message batching, compression, and partitioning
You may not always use Kafka for all projects you work on. But if you want to learn more, the docs page has helpful usage examples.
Wrapping Up
And that’s a wrap! We’ve gone over some of the most commonly used Python libraries for data engineering. If you want to explore data engineering, you can try building end-to-end data engineering projects to see how these libraries actually work.
Here are a couple of resources to get you started:
Happy learning!
Bala Priya C is a developer and technical writer from India. She likes working at the intersection of math, programming, data science, and content creation. Her areas of interest and expertise include DevOps, data science, and natural language processing. She enjoys reading, writing, coding, and coffee! Currently, she’s working on learning and sharing her knowledge with the developer community by authoring tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource overviews and coding tutorials.
