Data Science

How to Use Python for Data Engineering

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Data Engineering is one of the fastest-growing data-related fields. It focuses on connecting systems, collecting data from sources, and transforming raw data into usable information. With the growing reliance on data-driven decision-making, the need for skilled engineers has surged recently.

Big Data Engineer jobs are expected to grow 33% by 2030.

With such an increase in demand comes an ever-evolving list of required skills. One such skill that is gaining traction amongst professionals is Python programming language. Python for data engineering is critical for tasks like creating automation scripts and building ETL pipelines.

This article will discuss some of the most popular data engineering tools and Python libraries for data engineering. We will also list some project ideas you can use to gain real-world experience in Python for data engineering.

Data Engineering Vs. Data Science

Data Engineering and Data Science are two different disciplines that both use Python. While they often work together, their goals are slightly different. Let’s compare Data Engineer vs. Data Science.

data engineer vs data scientist

  • Common Data Engineering Skills

Knowledge of data engineering tools is valuable for businesses and organizations because it helps them gain insights from data. The common data engineering skills are:

data engineering skills

  1. Programming languages: Python, Java, and Scala. Python for Data Engineering is one of the vital skills needed in this industry to put up statistical models, build data pipelines, and conduct in-depth analyses on them. 
  2. Database: Knowledge of SQL and NoSQL databases like Cassandra, MongoDB, and HBase.
  3. Data Warehousing: Concepts behind data warehousing, particularly ETL (Extract, Transform, Load) procedures and data modeling.
  4. Data Integration: It combines data from many sources, including file systems, online services, and APIs.
  5. Big Data: Being familiar with distributed computing platforms like Hive, Hadoop, and Spark is crucial.
  6. Version Control: It will help branching, merging, tagging, and version control systems like Git.
  7. Data Visualisation Software: Skilled at using data visualization software like QlikView, Tableau, and Power BI.
  8. Cloud computing, storage, Azure, AWS, and GCP knowledge.
  9. Data pipelines can be constructed using tools like Apache NiFi, Apache Kafka, and Apache Airflow.
  10. Soft skills include strong communication capabilities, the ability to solve problems, and a willingness to learn.

Also read:

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Why Use Python for Data Engineering?

Python has become the go-to language for data engineering and analysis due to its versatility, scalability, and intuitive syntax.

  • It is a high-level programming language created to read and comprehend easily. Unlike lower-level languages such as C++ or Java, Python code tends to be shorter, more concise, and easier to debug.
  • In addition, various libraries make it easy for developers to work with datasets of any size. Libraries like NumPy, Pandas, and Scikit Learn allow developers to quickly manipulate large amounts of data with a few lines of code. This makes them ideal for complex tasks such as natural language processing (NLP), image recognition, and machine learning algorithms—all essential for data engineering.
  • Numpy and Pandas are two of the most commonly used libraries. They offer various functions, allowing developers to access, manipulate, and analyze large datasets easily. Scikit-learn is another popular library for machine learning and data analysis tasks. Users should also become familiar with SQL (Structured Query Language).

To get started with Python for data engineering, it is important to have a basic understanding of the language. After learning the fundamentals of Python, there are several key libraries you should become familiar with to use it for data engineering tasks effectively.

Also read: Master Python for Data Science: A Comprehensive Guide for Beginners

How is Python Used for Data Engineering?

Python’s vast library of modules provides powerful tools for building scalable architectures such as pipelines and ETL (extract-transform-load). This allows users to easily perform complex operations while leveraging the language’s full power.

Additionally, Python’s readability makes it easy to debug code quickly when issues arise. With its rich set of libraries and frameworks, Python is an ideal choice for data engineers looking to build reliable solutions tailored to their needs. It is used for the following:

#1 Data Acquisition

Data acquisition involves gathering data from various sources for later processing. Python provides many tools for working with structured and unstructured data, such as web scraping, access to APIs, and more. It also has libraries like Pandas that can manipulate datasets quickly and easily.

#2 Data Wrangling

Data wrangling involves cleaning and transforming data into a structured format, which is made easy with several Python libraries. These libraries simplify extracting, cleaning, and manipulating data with powerful and straightforward functions. Additionally, its ability to work with numerical and categorical data makes it well-suited.

#3 Custom Business Logic

With its simple syntax and powerful libraries, Python creates custom business logic. It allows developers to quickly develop scripts, APIs, and applications to process external data and present results meaningfully.

#4 Machine Learning

Python has become popular for machine learning due to its rich set of data science libraries, such as TensorFlow, Keras, Scikit-learn, Pytorch, etc. These libraries have made it easier for developers to build ML models with less hassle and quicker time.

Python also provides visualization tools such as Matplotlib or Seaborn, making it simple to interpret algorithm results. Additionally, its ability to work with numerical and categorical data makes it perfect for ML tasks.

Python offers comprehensive data engineering tools for managing, manipulating, and visualizing data, from raw data acquisition to building machine-learning models. With the right libraries and frameworks, businesses can create efficient solutions for actionable insights and better decisions.

Also read: What are the best Python Libraries for Machine Learning to learn in 2024?

Popular Python Libraries for Data Engineering

Python libraries are useful data engineering tools for manipulating data. These libraries offer powerful features such as numerical computation processing, data visualization, machine learning algorithms, and deep learning frameworks. These libraries can help simplify and automate various data engineering tasks like feature extraction or model training.

The most popular Python libraries used for data engineering are:

python libraries for data engineering

  • Pandas

Pandas is a highly popular Python library offering extensive data analysis and manipulation functionality. It provides a range of data structures such as Series, DataFrame, and Panel, enabling engineers to handle data easily.

Pandas support multiple data formats, including CSV, Excel, and SQL databases, making it an indispensable tool for data wrangling. Furthermore, it offers various data cleaning, filtering, grouping, and merging operations.

  • Apache Airflow

Apache Airflow is a widely used Python library for data engineers. It lets them to create and manage complex data processing pipelines and offers a platform for programmatically authoring, scheduling, and monitoring workflows.

Airflow provides a powerful interface for defining Directed Acyclic Graphs (DAGs) that specify the workflow structure. It enables users to easily manage multiple tasks and dependencies and includes features like task retries, error handling, and logging.

  • TensorFlow

TensorFlow is an open-source Python library that provides a comprehensive platform for creating and refining machine learning models. It supports deep neural networks, convolutional neural networks, and recurrent neural networks.

TensorFlow also includes model deployment, monitoring, and optimization tools. It has several uses, including speech and picture recognition and natural language processing.

Also read: Pytorch vs. TensorFlow: Which Framework to Choose?

  • PyParsing

PyParsing is a Python library that provides a powerful yet flexible framework for creating parsers. It allows developers to easily create parsers for complex text-based data formats such as programming languages, configuration files, etc.

PyParsing offers features like error reporting, whitespace handling, and built-in operators, enabling developers to create highly precise and accurate parsers.

  • Scikit-learn

Scikit-learn is a Python library that provides various machine learning and data mining tools. It offers various algorithms for classification, regression, clustering, and more. Scikit-learn provides a simple and efficient API that makes it convenient to create machine-learning models.

It also offers data preprocessing, model selection, and evaluation features, making it an essential tool for data engineers working with machine learning applications.

Also read:

Python Projects for Data Engineering 

Python offers numerous libraries and frameworks that provide a comprehensive toolset for building sophisticated software solutions. This makes Python ideal for developing data engineering projects that can store, process, and analyze large volumes of data.

Python projects for data engineering range from simple scripts that automate mundane tasks to complex applications that integrate multiple systems and technologies. Some examples are-

1) Real Estate Price Prediction

You can build a price prediction model for real estate using Python. The model would leverage a range of datasets, such as economic indicators, population and demographic data, industry trends, etc., and use statistical methods to analyze the data and generate accurate predictions about future prices. 

  1. Start by importing the required libraries, such as Pandas, NumPy, Matplotlib, and Scikit-Learn.
  2. Next, load the historical data for the prices you want to predict. This data should contain the prices and any other relevant features.
  3. Preprocess and clean the data by handling missing values, scaling, and normalization.
  4. Split the processed data into training sets to train the model and testing sets for evaluation. Use Scikit-learn’s Linear Regression class to train the model on the training data. To train the model, you can use linear regression algorithms like Elastic Net, OLS Estimator, or Lasso and Ridge Regressors
  5. Evaluate the model’s performance using metrics such as Mean Squared Error (MSE) or R-squared to evaluate the model.
  6. Compare each model using R-squared values, which are preferred above 0.7 and should not exceed 0.6.

2) Data Modeling for Multi-user Access on Streaming Platforms

Build a model that denies screens to unpaid users or those who have exceeded their monthly quota.

  1. Start by defining a Python data model to represent the OTT user. It must include attributes like username, password, role, and privileges.
  2. Next, define the roles and privileges available to OTT users and map them to the user model.
  3. The model will retrieve the user’s credentials, such as username and password, from the input and authenticate the user by verifying the credentials against the user model.
  4. Once authenticated, the model must check the user’s privileges against the required privileges for the requested operation. 
  5. If the requested operation involves multiple users, the model should check if all users have paid for access.
  6. If any user has not paid, the model will deny access and return an error message. If the user has the required privileges and all users have paid, the model will grant access to the requested operation.

3) Streamline the Process of Medicine Labeling Globally

Labeling medicines is essential to meeting various global and local standards. Python can automate this process, making it easier and more cost-effective for organizations to adhere to the standards set by different regulatory bodies. 

  1. Begin by collecting all the relevant data related to medicine labeling from different sources such as regulatory authorities, pharmaceutical companies, and medical research institutions.
  2. Clean the collected data to eliminate errors, missing values, and inconsistencies to get accurate and consistent data.
  3. Merge the data from different sources to create a comprehensive database of medicine labeling information. Natural language processing (NLP) techniques in Python are used to process large volumes of data and create an automated process for labeling medicines. Python’s machine learning libraries, such as Scikit-Learn, can help you build models with higher accuracy rates.
  4. Data analysis techniques such as statistical analysis and data visualization extract insights and trends from the data. Develop a data model using Python that can process the data, extract information, and generate labels for medicines. Validate the data model by testing it with different datasets to ensure accuracy and efficiency.
  5. Deploy the data model in the cloud or on-premises to make it accessible to users globally.

The model must be continuously improved by incorporating new data sources and updating the algorithms to ensure efficiency.

4) Exploratory Analysis of Geolocational Data

Python can explore geographical datasets using libraries like Geopandas and Shapely. This kind of project could be used to uncover insights from satellite imagery or street data.

  1. Start by collecting high-quality data, as the accuracy of your model depends on the quality of the data you use. Make sure to collect reliable data that includes accurate location information.
  2. Preprocess your data by cleaning, removing duplicates, and converting it into a format compatible with your model.
  3. Choose a suitable machine learning algorithm to predict geographic locations. You may want to try algorithms such as k-nearest neighbors (KNN), support vector machines (SVM), or random forests.
  4. Split your data into training and testing sets. Use one dataset to train your model. You can adjust the hyperparameters of your algorithm to improve its accuracy. Use the other dataset to evaluate its accuracy.
  5. Adjust your algorithm’s hyperparameters or try a different algorithm to tune your model to achieve precision. Once satisfied, it is deployed to predict geographic locations.

You may want to build a user interface or API to make it easier for users to input data and get predictions.

Also read: Understanding Exploratory Data Analysis in Python

5) Build a Recommendation System

A data model will be used to build a recommendation system to collect data about users’ preferences and interactions to generate similar responses to their queries.

  1. Collect user data, including their preferences, ratings, and interactions with the system.
  2. The data can be collected through external sources such as social media, purchase history, or search history to enhance the recommendation system’s performance.
  3. The collected data may contain missing values, outliers, or duplicates. Preprocess the data by removing duplicates, filling in missing values, and scaling the data.
  4. Pick a recommendation algorithm for the model, such as collaborative filtering, content-based filtering, or hybrid recommendation systems.
  5. Train the recommendation model using the defined algorithm and evaluate its performance using accuracy, precision, recall, and F1-score metrics.
  6. Fine-tune the model parameters to improve its performance. Once the model is optimized and validated, it will be deployed in a production environment to serve real-time recommendations to users.

Keep updating the recommendation model with new data to ensure the accuracy and relevance of the results over time.

Use Cases of Python for Data Engineering

Python is widely used in data engineering due to its versatility and extensive libraries. Here are some common use cases:

1) Querying Data from Database

Python has many open-source libraries, making it the best tool for data engineers. One of its most prominent features is its ability to manipulate and query various types of databases.

In this world of massive datasets, data analysts and data engineers require the ability to query relational databases (RDBMS). RDBMS helps perform various tasks like creating, reading, updating, and deleting databases.

This is where Python libraries come into play. Different databases require different Python libraries for connection. One of the most used libraries is “mysql-connector” to connect to MySQL database.

To install this library, you can use the “pip” command. Follow the instructions below on your command line.

 C:\Users\prana>pip install mysql-connector 

Once you have installed the required library, connect it to MySQL. Follow the code below:

import mysql.connector
try:
conn = mysql.connector.connect(
host='db_hostname',
user='db_username',
passwd='db_password',
database='db_name'
)
print("Database connection successful")
except Exception as e:
print(f"Error: '{e}'")

Here, you are trying to establish a connection with the database. You need database credentials like a hostname, username, password, and database name. You are using the MySQL connector library to connect with the database.

Query the database table with the “Select” command. Follow the code below:

cursor = conn.cursor()
query = "SELECT * FROM table_name"
cursor.execute(query) myresult = cursor.fetchall()
for x in myresult: print(x)

You query the database using “conn.cursor()”. You then execute the query using the “cursor.execute(query)” command. The “Select” query will return all the records in the table. You then store the fetched records in the “myresult” variable using the “cursor.fetchall()”. You can print them one by one.

This allows you to add, update, and delete records in a database and manipulate them using simple SQL queries and Python.

2) Sending Email Alerts with Full Error Codes

To send email alerts in Python, you need the smtplib library.

To install this library, you can use the “pip” command. Follow the instructions below on your command line.

 C:\Users\prana>pip install secure-smtplib 

Let’s get started with the code.

In the image below, you first import smtplib and EmailMessage from the email library. You define credentials such as the email body, subject, email IDs for sender and receiver, and the sender’s email password.

Then, you connect to the Gmail server using “smtp.gmail.com” and port 587. You log in to Gmail and send a message to the receiver. Once the message is sent, you close the connection. This way, you can send alerts with full error codes to the receiver.

python for data engineering

3) Writing CSV File into a Database

Data engineers require an easy and efficient way to read CSV files with huge datasets and the ability to manipulate and store data inside them. Python has two well-known libraries that help data engineers with this task.

Those are the pandas and MySQL connectors. “Pandas” is the best Python library for reading CSVs and data manipulation. “MySQL-connector” is used to make connections to MySQL databases using Python.

To install these libraries, you can use the “pip” command. Follow the instructions below on your command line.

 C:\Users\prana>pip install mysql-connector   C:\Users\prana>pip install pandas 

Now let us see how we are going to use these libraries for reading a CSV and storing the data in a database:

import pandas as pd

import mysql.connector

df = pd.read_csv(r"filepath\student.csv")

python for data engineering

In the code above, you first import the pandas and mysql-connector libraries. Then, you read the CSV file using Pandas’ “read_csv” function. You must pass a file path in the read_csv function. The output resembles the one in the picture.

It displays a DataFrame with all the columns and rows from the CSV file. Using pandas, you can perform various operations, such as updating and manipulating data.

After reading the CSV file, you connect to the MySQL database using the mysql-connector library, as shown below.

python for data engineering

Once the connection has been established with the database, you need to store the data from the CSV file in the database using an “insert” query. You can do this by iterating over the dataframe you created and storing it row-wise in the database.

After installing the ‘requests’ library, you first import it. You define the URL of the website from which you want to pull the data. Depending on the number of pages you want to extract data from, set the range and pull data from the website using iteration. For data extraction, use the requests.get() function, appending each page number to the URL. You then convert the data to JSON format.

4) Retrieving Multiple Pages of Data From a REST API

REST APIs, or Representing State Transfer APIs, are used to access and transfer data via HTTP requests. They have various principles, such as a uniform interface, client-server, statelessness, layered systems, etc. You can pull data from websites using REST APIs.

You can use Python’s ‘requests’ library to make requests to a website.

To install this library, you can use the “pip” command. 

Follow the instructions below on your command line.

 C:\Users\prana>pip install requests 

import requests

import requests

for page in range(1,5):

url = 'https://api.safecast.org/en-US/measurements.json?page={}'

data = requests,get(url.format(page))

print(data.json())

After installing the ‘requests’ library, you first import it. Define the website URL from which you want to pull the data. Depending on the number of pages you want to extract data from, set the range and use iteration to pull data from the website. For data extraction, use the requests.get() function, appending each page number to the URL. Then, convert the data to JSON format.

5) Transformation of REST API JSON Results for Database Insertion

REST APIs accept requests in JSON format and can also return responses in JSON format. JSON stands for Javascript Object Notation. It is a standard for exchanging data on computers.

To transform REST API JSON data for storage in the database, you can use Pandas and MySQL-connector again.

To install these libraries, you can use the “pip” command. 

Follow the instructions below on your command line.

 C:\Users\prana>pip install mysql-connector   C:\Users\prana>pip install pandas 

To start with the code, we first import both libraries.

import pandas as pd

import mysql-connector

After this, you convert JSON data to pandas dataframe. Here, you will use the json_normalize() function of pandas to flatten the nested JSON. Refer to the below code for more understanding:

python for data engineering

After flattening the nested JSON, the output will look like this:

python for data engineering

Once flattened and converted to a dataframe, you can connect to the MySQL database and insert data row-wise, as explained in the previous sections.

6) Data Visualization Using Python

Data visualization is one of the most important aspects data analysts and engineers use. Graphs and plots help data engineers understand the various parameters of data and how they vary.

Matplotlib is the best Python library for data visualization. It provides a wide range of 2D plots, such as lines, bars, scatter plots, histograms, etc. Let’s study Matplotlib in detail.

To install this library, you can use the “pip” command. 

Follow the instructions below on your command line.

 C:\Users\prana>pip install matplotlib 

Let’s see a few plots one by one:

Line Plot: A line plot is a plot in which data is represented in series on a number line, with each value being the frequency.

In the below picture, we first import the Matplotlib library. Then you define values on the x and y axes and plot the line plot using the plt.plot(x,y) function.

line plot in python visualization

line plot in python visualization

Bar Plot: A bar plot is a graph in which categorical data is represented by rectangular bars.

For the bar plot, you need to define values on the x and y axes and plot the bar plot using the plt.bar(x,y) function.

bar plot in python visualization

bar plot in python visualization

Scatter Plot: A scatter plot portrays the relationship between two values as dots.

For the scatter plot, you need to define values on the x and y axes and plot the scatter plot using the plt.scatter(x,y) function.

scatter plot in python visualization

scatter plot in python visualization

Histogram: A histogram is a plot in which numeric data is plotted as rectangular bars.

You require only one parameter for the scatter plot and plot the histogram using the plt.hist(param) function.

histogram in python visualization

histogram in python visualization

Also read: How To Visualize Data Using Python

Conclusion

Python is a powerful language used to solve many data engineering problems. It offers great flexibility in coding, making it suitable for both small and large-scale projects.

Python provides easy and intuitive access to SQL databases, allowing developers to manipulate large datasets with complex queries quickly. Additionally, its expansive library of packages makes it highly customizable and extendable for various use cases. 

All these features make Python an essential tool for any data engineer looking to automate their workflow efficiently. With the rise of big data technologies, Python will continue to grow as a popular programming language and remain an invaluable asset for data engineers worldwide. 

FAQs

  • How is Python used for data engineering?

Python is widely used for data engineering due to its diverse libraries and frameworks that support data wrangling, data integration, data transformation, and more. 

With libraries like Pandas, NumPy, and PySpark, Python provides powerful data acquisition, processing, and analysis tools. Python can also handle large volumes of data and is highly scalable, making it a popular choice for big data applications. 

Python’s flexibility also allows data engineers to customize data pipelines to their specific needs, while its open-source nature enables a thriving community of developers to create and share new resources and tools.

  • Is Python enough for data engineering?

Python is an essential tool for data engineering, but it’s not always enough. A combination of hard and soft skills is needed. 

To become a successful data engineer, you must understand programming languages like SQL and Java. Knowledge of databases, data modeling, ETL tools, and data warehousing is also crucial. 

Familiarity with big data technologies, like Hadoop and Spark, is a valuable asset. In addition to technical expertise, a data engineer should have strong soft skills, including communication, problem-solving, and attention to detail.

  • Is Pandas used for ETL?

Pandas is often used for ETL (Extract, Transform, and Load) processes. It is a highly popular Python library for data analysis and manipulation and provides various functions for cleaning, filtering, and transforming data. 

Pandas can also handle multiple data formats, making them a valuable tool for extracting data from various sources, transforming it into the desired format, and loading it into a target system.

  • Is data engineering a coding job?

Yes, data engineering is a coding job. Data engineers use programming languages like Python, Java, or Scala to design, build, and maintain data pipelines and infrastructure. They also use various software tools and platforms, such as Apache Spark, Hadoop, and SQL databases, which require knowledge of coding and scripting.

Data engineers must also have a solid understanding of databases, distributed systems, and data processing frameworks. While data engineering involves a fair amount of coding, it also requires strong analytical and problem-solving skills and collaboration with other stakeholders in the data ecosystem.

Nidhi is currently working with the content and communications team of AnalytixLabs, India’s premium edtech institution. She is engaged in tasks involving research, editing, and crafting blogs and social media content. Previously, she has worked in the field of content writing and editing. During her free time, she indulges in staying updated with the latest developments in Data Science and nurtures her creativity through music practice

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