Top 20+ Data Engineering Projects to Solve Real-World Problems

In the modern world, data has become an integral part of almost every aspect of the day-to-day management processes. However, without the ability to structure the information effectively, data is worthless.

For a business to operate and provide value, it must harness data to analyse market trends, improve processes, and interact with clients efficiently. This is where data engineering comes in to provide the backbone architecture that ensures the development and maintenance work required for the seamless analysis and reporting of data.

In this post, we are going to provide practical project proposals for graduates, practitioners in the middle of their careers, and even experienced professionals. These projects will facilitate your self-improvement, and greatly enhance career prospects as a data engineer. Let’s explore it!

What Is Data Engineering?

Data engineering is the foundation of efficient data management. Data management requires the construction of systems and pipelines that receive, keep, and process data from various sources. These systems ensure that the raw material data is processed and transformed to the desired structures, which are appropriate for analysis and decision making.

In their profession, a data engineer develops and provides support for the architecture that executes large-volume data tasks. This involves the construction of pipelines, the administration of databases, and the use of big data processing technologies. Another objective for data engineers is to focus on optimizing performance and ensuring data quality that meets business requirements.

The society we live in today is data-driven and data engineering helps organizations make use of their data efficiently. It ensures data is accessible, accurate, and ready to support critical insights.

Engaging in data engineering projects serves to develop your knowledge, practical skills, and in turn, boost your employability.

• Acquire practical knowledge of tools such as SQL, Python and cloud interfaces.
• Learn to design and execution of realistic data pipeline management.
• Develop a personal portfolio which evidences your professionalism in technical aspects.
• Increase your analytical and creative skills in approaches and solutions to issues.
• Learn how data moves through businesses from initiation to the final stage.
• Prepare yourself for positions such as Data Engineer or Big Data Expert.
• Grow your professional knowledge with new trends and technologies within the industry.

8 Beginner-level Data Engineering Projects

Data Collection and Storage System

Creating a Data Collection and Storage System is an excellent beginner project in data engineering. This project involves gathering data from various sources and storing it in an organized manner for easy access and analysis.

Steps to Complete the Project

• Identify Data Sources: Choose sources like public APIs, websites, or CSV files.
• Data Extraction: Use Python libraries such as requests or BeautifulSoup to collect data.
• Data Cleaning: Remove errors and inconsistencies using Pandas.
• Storage Setup: Use a simple database like SQLite or a cloud service like AWS RDS to store the data.
• Automation: Write scripts to regularly collect and store data automatically.

Tech Stack

• Programming Language: Python
• Libraries: Requests, BeautifulSoup, Pandas
• Database: SQLite or AWS RDS
• Automation Tools: Cron jobs or Python scripts

Skills Developed

• Data extraction and cleaning
• Database management
• Scripting for automation

Data Quality Monitoring System

Creating a Data Quality Monitoring System can be the best first project to start with as it shows how to verify, assess, and control the data being collected and used. This project requires putting in place methods that will enable continuous scanning of the data for errors, inconsistencies and any irregularities.

Steps to Complete the Project

• Define Quality Metrics: Determine what constitutes data quality for your dataset (e.g., completeness, accuracy).
• Data Ingestion: Collect data from chosen sources using APIs or file uploads.
• Implement Validation Rules: Create rules to check for missing values, duplicates, and data type mismatches.
• Alert System: Set up notifications for when data quality issues are detected.
• Reporting Dashboard: Develop a simple dashboard to visualize data quality metrics over time.

Tech Stack

• Programming Language: Python
• Libraries: Pandas, NumPy
• Database: PostgreSQL or MySQL
• Visualization Tools: Tableau or Power BI
• Alerting Tools: Slack API or email notifications

Skills Developed

• Data validation and cleansing
• Automation of quality checks
• Dashboard creation for monitoring
• Handling alerts and notifications

ETL Pipeline for Sales Data

Constructing an ETL Pipeline for Sales Data enables you to grasp the entire process of data sourcing, data cleansing, and data warehousing. This project is quite important in the administration and analysis of sales details.

Steps to Complete the Project

• Extract: Gather sales data from sources such as CSV files, APIs, or databases.
• Transform: Clean the data by handling missing values, standardizing formats, and aggregating metrics.
• Load: Insert the transformed data into a target database or data warehouse.
• Scheduling: Automate the ETL process to run at regular intervals.
• Monitoring: Implement checks to ensure the pipeline runs smoothly without errors.

Tech Stack

• Programming Language: Python
• ETL Tools: Apache Airflow or Talend
• Database: PostgreSQL or Amazon Redshift
• Libraries: Pandas, SQLAlchemy
• Scheduling Tools: Cron jobs or Airflow schedulers

Skills Developed

• Building and managing ETL pipelines
• Data transformation and cleaning
• Database management and optimization
• Automation and scheduling of data workflows

Real-time Data Processing System

Building a Real-Time Data Processing System makes it easier for you to work with real-time streaming data when performing sequential analyses. This project is important for systems which have to provide a quick perspective, for instance, of a monitoring system or a real-time dashboard.

Steps to Complete the Project

• Data Source Identification: Choose a real-time data source like social media streams, IoT devices, or live transactions.
• Stream Processing Setup: Use tools to ingest and process data in real-time.
• Data Transformation: Apply necessary transformations, such as filtering, aggregation, or enrichment, on the incoming data.
• Storage: Store the processed data in a real-time database or data warehouse.
• Visualization: Create live dashboards to display the processed data and insights.

Tech Stack

• Programming Language: Python or Java
• Stream Processing Tools: Apache Kafka or Apache Flink
• Data Storage: MongoDB or Elasticsearch
• Visualization Tools: Grafana or Kibana
• Libraries: Kafka-Python, PySpark

Skills Developed

• Real-time data ingestion and processing
• Working with stream processing frameworks
• Implementing data transformations on the fly
• Building live data visualization dashboards

Recommendation System

Building a Recommendation System is a fantastic beginner project that introduces you to personalized data delivery. This system suggests products or content based on user preferences and behaviour.

Steps to Complete the Project

• Collect Data: Use a dataset containing user interactions, such as ratings or purchase history.
• Data Preprocessing: Clean the data by handling missing values and normalizing information.
• Choose a Model: Implement a simple collaborative filtering algorithm to generate recommendations.
• Build the System: Develop a script that takes user input and provides relevant suggestions.
• Evaluate Performance: Test the system’s accuracy by comparing recommendations with actual user preferences.

Tech Stack

• Programming Language: Python
• Libraries: Pandas, NumPy, Scikit-learn
• Database: SQLite or CSV files
• Framework: Flask (optional for creating a simple web interface)

Skills Developed

• Understanding recommendation algorithms
• Data cleaning and preprocessing
• Basic machine learning implementation
• Building user-centric applications

Log Analysis Tool

Creating a Log Analysis Tool helps you learn how to process and interpret log files from applications or servers. This project is essential for monitoring system performance and troubleshooting issues.

Steps to Complete the Project

• Collect Logs: Gather log files from web servers or applications.
• Parse Logs: Use scripts to extract relevant information such as timestamps, error codes, and user actions.
• Store Data: Save the parsed data in a structured format like a SQL database.
• Analyze Patterns: Identify common errors, peak usage times, and other trends.
• Visualize Results: Create charts or dashboards to display the analysis findings.

Tech Stack

• Programming Language: Python
• Libraries: Pandas, Regex, Matplotlib
• Database: MySQL or PostgreSQL
• Visualization Tools: Tableau or Power BI

Skills Developed

• Log file parsing and processing
• Database management
• Data analysis and pattern recognition
• Creating visual reports

Data Warehouse Solution

Developing a Data Warehouse Solution introduces you to storing and managing large volumes of data from different sources in a centralized repository.

Steps to Complete the Project

• Identify Data Sources: Select multiple sources such as databases, APIs, or flat files.
• Design Schema: Create a schema that organizes data efficiently, often using star or snowflake models.
• Extract Data: Use ETL (Extract, Transform, Load) processes to gather data from sources.
• Transform Data: Clean and format the data to fit the warehouse schema.
• Load Data: Insert the transformed data into the data warehouse.
• Query and Analyze: Use SQL to run queries and generate reports from the warehouse.

Tech Stack

• Programming Language: SQL, Python
• ETL Tools: Apache Airflow or Talend
• Database: Amazon Redshift, Google BigQuery, or Snowflake
• Visualization Tools: Tableau or Power BI

Skills Developed

• Data warehousing concepts
• ETL pipeline creation
• Schema design and database management
• Advanced SQL querying

Weather Data Aggregation

Creating a Weather Data Aggregation project allows you to collect and compile weather information from various sources for analysis and visualization.

Steps to Complete the Project

• Select Data Sources: Use public APIs like OpenWeatherMap or WeatherAPI to gather weather data.
• Data Extraction: Write scripts to fetch data at regular intervals.
• Data Cleaning: Handle missing values and standardize data formats.
• Store Data: Save the aggregated data in a database or cloud storage.
• Analyze Trends: Identify patterns such as temperature changes or precipitation levels over time.
• Visualize Data: Create graphs or dashboards to display the weather trends.

Tech Stack

• Programming Language: Python
• Libraries: Requests, Pandas, Matplotlib
• Database: SQLite or AWS S3
• Visualization Tools: Tableau or Power BI

Skills Developed

• API integration and data fetching
• Data cleaning and normalization
• Time-series analysis
• Data visualization techniques

Web Scraping for E-commerce

Building a Web Scraping for E-commerce project teaches you how to extract product information from online stores for analysis or comparison.

Steps to Complete the Project

• Choose a Website: Select an e-commerce site to scrape, ensuring compliance with their terms of service.
• Identify Data Points: Determine which data to extract, such as product names, prices, and reviews.
• Write Scraping Scripts: Use tools to navigate and extract the desired information.
• Data Cleaning: Remove duplicates and irrelevant data to ensure accuracy.
• Store Data: Save the scraped data in a structured format like a database or CSV file.
• Analyze Data: Compare prices, track product availability, or analyze customer reviews.

Tech Stack

• Programming Language: Python
• Libraries: BeautifulSoup, Scrapy, Selenium
• Database: MongoDB or SQLite
• Storage: CSV files or cloud storage solutions

Skills Developed

• Web scraping techniques
• Handling dynamic web content
• Data cleaning and storage
• Ethical considerations in data extraction

Data Visualization Dashboard

Developing a Data Visualization Dashboard allows users to access insights from data in a more engaging way, while making complex data easier to grasp.

Steps to Complete the Project

• Select a Dataset: The first step involves picking a dataset based on a field of your choice, for instance, sales data or the data depicting user actions.
• Data Cleaning and Data Preprocessing: Check if the data collected is reliable and in the right format for visual presentation.
• Other Possible Tools for Visualization: Choose the preferable tools like Tableau, Power BI, or the Python libraries.
• Design the Dashboard: Select and design suitable charts, graphs and other forms of visual representation for the key figures.
• Implement Interactivity: For example, place filters, slicers and other components in the dashboard to give users the opportunity to interact with the dashboard.
• Deploy the Dashboard: Share your dashboard online or within your organization for access.

Tech Stack

• Programming Language: Python or use GUI-based tools
• Libraries/Tools: Tableau, Power BI, Plotly, Dash
• Database: SQL or Excel for data storage
• Web Hosting (optional): Heroku or GitHub Pages for deployment

Skills Developed

• Data visualization principles
• Using visualization tools effectively
• Designing user-friendly interfaces
• Presenting data-driven insights clearly

7 Intermediate-level Data Engineering Projects

Data Warehousing with Redshift

Building a Data Warehousing with Redshift project introduces you to centralized data storage solutions, enabling efficient data analysis and reporting.

Steps to Complete the Project

• Set Up AWS Redshift: Create an AWS account and set up a Redshift cluster.
• Design Schema: Plan a star or snowflake schema based on your data requirements.
• Extract Data: Gather data from various sources such as CSV files, APIs, or databases.
• Transform Data: Clean and format the data using ETL tools or Python scripts.
• Load Data: Import the transformed data into Redshift using COPY commands or ETL pipelines.
• Query and Analyze: Use SQL to perform queries and generate reports from the data warehouse.

Tech Stack

• Cloud Platform: Amazon Web Services (AWS)
• Data Warehouse: Amazon Redshift
• ETL Tools: Apache Airflow, Python
• Database Tools: SQL Workbench/J
• Visualization Tools: Tableau or Power BI

Skills Developed

• Data warehousing concepts and design
• Proficiency with AWS Redshift
• ETL pipeline creation and management
• Advanced SQL querying
• Data analysis and reporting

Stream Data with Kafka

Creating a Stream Data with Kafka project helps you understand real-time data processing and streaming technologies.

Steps to Complete the Project

• Install Kafka: Set up Apache Kafka on your local machine or a cloud server.
• Create Topics: Define Kafka topics for different data streams.
• Produce Data: Develop producers to send data to Kafka topics using APIs.
• Consume Data: Build consumers to read and process data from the topics in real-time.
• Process Streams: Implement data processing logic, such as filtering or aggregating, using Kafka Streams or other frameworks.
• Monitor and Scale: Set up monitoring tools to track performance and scale the Kafka cluster as needed.

Tech Stack

• Programming Language: Java or Python
• Streaming Platform: Apache Kafka
• Processing Frameworks: Kafka Streams, Apache Flink
• Monitoring Tools: Prometheus, Grafana
• Deployment: Docker or Kubernetes (optional)

Skills Developed

• Real-time data streaming and processing
• Kafka cluster setup and management
• Building producers and consumers
• Stream processing techniques
• Monitoring and scaling streaming applications

Customer Churn Prediction

Developing a Customer Churn Prediction project allows you to apply data engineering and machine learning to predict customer behavior.

Steps to Complete the Project

• Data Collection: Gather customer data from CRM systems or datasets available online.
• Data Cleaning: Handle missing values, outliers, and normalize the data.
• Feature Engineering: Create relevant features that can influence churn, such as usage patterns or customer service interactions.
• Build ETL Pipeline: Extract, transform, and load the data into a data warehouse or database.
• Model Training: Use machine learning algorithms to train a churn prediction model.
• Deploy Model: Integrate the model into a pipeline for real-time or batch predictions.
• Evaluate Performance: Assess the model’s accuracy and refine as necessary.

Tech Stack

• Programming Language: Python
• Machine Learning Libraries: Scikit-learn, Pandas
• Data Warehouse: PostgreSQL or AWS Redshift
• ETL Tools: Apache Airflow
• Visualization Tools: Tableau or Power BI

Skills Developed

• Data preprocessing and feature engineering
• Building and managing ETL pipelines
• Applying machine learning for predictive analytics
• Model deployment and integration
• Performance evaluation and optimization

Real-Time Data Visualization

Creating a Real-Time Data Visualization project enables you to display live data insights interactively.

Steps to Complete the Project

• Select Data Source: Choose a real-time data source such as social media feeds, sensor data, or live transactions.
• Set Up Data Stream: Use tools like Apache Kafka or WebSockets to stream data.
• Process Data: Implement real-time data processing using frameworks like Apache Spark or Flink.
• Build Visualization Dashboard: Use visualization tools to create dynamic charts and graphs that update in real-time.
• Integrate Frontend: Develop a frontend interface using JavaScript frameworks like React or Vue.js for interactive visualizations.
• Deploy Dashboard: Host the dashboard on a cloud platform for accessibility.

Tech Stack

• Programming Language: JavaScript, Python
• Streaming Tools: Apache Kafka, WebSockets
• Processing Frameworks: Apache Spark, Apache Flink
• Visualization Tools: Grafana, Kibana, Plotly
• Frontend Frameworks: React, Vue.js
• Deployment: AWS, Heroku

Skills Developed

• Real-time data streaming and processing
• Building interactive visualization dashboards
• Frontend development for data presentation
• Integrating backend and frontend systems
• Deploying and maintaining live applications

IoT Data Collection and Analysis

Developing an IoT Data Collection and Analysis project allows you to work with data generated from Internet of Things devices.

Steps to Complete the Project

• Choose IoT Devices: Select sensors or devices that generate data, such as temperature sensors or smart meters.
• Set Up Data Collection: Connect devices to a network and configure them to send data to a central repository.
• Data Ingestion: Use platforms like MQTT or HTTP APIs to collect data streams.
• Store Data: Save the incoming data in a database or data lake for analysis.
• Process and Analyze: Clean and analyze the data to extract meaningful insights using Python or SQL.
• Visualize Results: Create dashboards to monitor IoT data in real-time and identify trends or anomalies.

Tech Stack

• Programming Language: Python, JavaScript
• IoT Protocols: MQTT, HTTP APIs
• Data Ingestion Tools: Node-RED, Apache NiFi
• Database: InfluxDB, MongoDB
• Visualization Tools: Grafana, Power BI

Skills Developed

• IoT device setup and data collection
• Real-time data ingestion and storage
• Data cleaning and analysis
• Building dashboards for IoT data
• Handling time-series data

Batch Processing with Spark

Creating a Batch Processing with Spark project teaches you how to handle large-scale data processing efficiently.

Steps to Complete the Project

• Set Up Apache Spark: Install and configure Spark on your local machine or a cloud environment.
• Choose Dataset: Select a large dataset that requires batch processing, such as logs or transaction data.
• Data Ingestion: Load the dataset into Spark using DataFrames or RDDs.
• Transform Data: Perform transformations like filtering, aggregating, and joining using Spark’s APIs.
• Optimize Performance: Use Spark’s optimization techniques to enhance processing speed and efficiency.
• Output Results: Save the processed data to a database, file system, or data warehouse for further analysis.
• Schedule Jobs: Automate batch processing tasks using scheduling tools like Apache Airflow.

Tech Stack

• Programming Language: Python, Scala, or Java
• Big Data Framework: Apache Spark
• Data Storage: HDFS, Amazon S3, or Azure Blob Storage
• ETL Tools: Apache Airflow
• Cluster Management: YARN, Kubernetes

Skills Developed

• Large-scale data processing with Spark
• Data transformation and aggregation techniques
• Performance tuning and optimization in Spark
• Automating batch workflows
• Integrating Spark with various data storage solutions

Data Modelling with DBT and BigQuery

Developing a Data Modelling with DBT and BigQuery project introduces you to modern data transformation and modeling techniques.

Steps to Complete the Project

• Set Up BigQuery: Create a Google Cloud account and set up a BigQuery project.
• Install DBT: Install Data Build Tool (DBT) on your local machine.
• Connect DBT to BigQuery: Configure DBT to interact with your BigQuery data warehouse.
• Design Data Models: Create SQL-based models to transform raw data into structured, analysis-ready tables.
• Implement Transformations: Use DBT’s features like macros and tests to manage and validate data transformations.
• Run and Schedule Models: Execute DBT models to apply transformations and schedule them for regular updates.
• Document and Test: Document your data models and implement tests to ensure data quality and integrity.

Tech Stack

• Cloud Platform: Google Cloud Platform (GCP)
• Data Warehouse: Google BigQuery
• Data Transformation Tool: DBT (Data Build Tool)
• Programming Language: SQL, Python (optional for macros)
• Version Control: Git

Skills Developed

• Data modeling and transformation with DBT
• Managing data workflows in BigQuery
• Writing and optimizing SQL queries
• Implementing data testing and documentation
• Automating data transformation pipelines

6 Advanced-level Data Engineering Projects

Advanced ETL Pipeline

Building an Advanced ETL Pipeline enhances your ability to handle complex data workflows efficiently.

Steps to Complete the Project

• Define Requirements: Identify data sources, destinations, and transformation needs.
• Choose ETL Tools: Select robust tools like Apache NiFi or AWS Glue.
• Extract Data: Connect to multiple data sources such as APIs, databases, and flat files.
• Transform Data: Implement complex transformations, including data enrichment and aggregation.
• Load Data: Transfer the transformed data to target systems like data warehouses or lakes.
• Automate Workflow: Schedule ETL jobs using tools like Apache Airflow.
• Monitor and Optimize: Set up monitoring to track pipeline performance and make necessary optimizations.

Tech Stack

• ETL Tools: Apache NiFi, AWS Glue
• Programming Language: Python, SQL
• Orchestration: Apache Airflow
• Data Storage: Amazon Redshift, Google BigQuery

Skills Developed

• Designing scalable ETL workflows
• Advanced data transformation techniques
• Automation and scheduling of data processes
• Performance monitoring and optimization

Distributed System for Big Data

Creating a Distributed System for Big Data teaches you how to manage and process large datasets across multiple machines.

Steps to Complete the Project

• Set Up Cluster: Install and configure a distributed computing framework like Hadoop or Spark.
• Data Ingestion: Load large datasets into the cluster using tools like Apache Flume or Kafka.
• Data Storage: Use distributed storage systems such as HDFS or Amazon S3.
• Process Data: Implement data processing jobs to perform tasks like sorting, filtering, and aggregating.
• Optimize Performance: Tune cluster settings for efficient resource utilization.
• Deploy Applications: Run distributed applications and monitor their performance.
• Ensure Fault Tolerance: Configure the system to handle node failures gracefully.

Tech Stack

• Framework: Apache Hadoop, Apache Spark
• Storage: HDFS, Amazon S3
• Data Ingestion: Apache Flume, Apache Kafka
• Monitoring Tools: Prometheus, Grafana

Skills Developed

• Setting up and managing distributed clusters
• Processing large-scale data efficiently
• Optimizing distributed system performance
• Ensuring system reliability and fault tolerance

Machine Learning Model Deployment

Deploying a Machine Learning Model integrates data engineering with machine learning to provide actionable insights.

Steps to Complete the Project

• Select a Model: Choose a machine learning model relevant to your data, such as a regression or classification model.
• Prepare Data: Ensure data is clean and properly formatted for the model.
• Train the Model: Use libraries like Scikit-learn or TensorFlow to train your model.
• Create an API: Develop an API using Flask or FastAPI to serve the model predictions.
• Containerize the Application: Use Docker to package the application for consistent deployment.
• Deploy to Cloud: Host the containerized application on platforms like AWS, Azure, or Google Cloud.
• Monitor Performance: Implement monitoring to track the model’s performance and usage.

Tech Stack

• Programming Language: Python
• Machine Learning Libraries: Scikit-learn, TensorFlow
• Web Framework: Flask, FastAPI
• Containerization: Docker
• Cloud Platforms: AWS, Azure, Google Cloud

Skills Developed

• Training and fine-tuning machine learning models
• Developing and deploying APIs
• Containerization and cloud deployment
• Monitoring and maintaining deployed models

Data Governance and Quality Check

Implementing Data Governance and Quality Check ensures that data remains accurate, secure, and compliant.

Steps to Complete the Project

• Define Data Policies: Establish rules for data access, usage, and management.
• Data Cataloging: Create a catalog to document data sources, metadata, and lineage.
• Implement Quality Checks: Develop scripts to validate data accuracy, completeness, and consistency.
• Set Up Access Controls: Use role-based access to secure sensitive data.
• Automate Governance Tasks: Schedule regular audits and quality checks using automation tools.
• Create Reporting Dashboards: Visualize data quality metrics and governance compliance.
• Ensure Compliance: Align data practices with regulations like GDPR or HIPAA.

Tech Stack

• Data Catalog Tools: Apache Atlas, Alation
• Programming Language: Python, SQL
• Automation Tools: Apache Airflow
• Visualization Tools: Tableau, Power BI

Skills Developed

• Establishing data governance frameworks
• Implementing data quality validation
• Securing data with access controls
• Automating governance and compliance tasks

Real-Time Fraud Detection

Developing a Real-Time Fraud Detection system helps identify and prevent fraudulent activities as they occur.

Steps to Complete the Project

• Collect Data: Gather transaction data from sources like databases or APIs.
• Data Cleansing: Remove bad values, duplicates and normalize the data for analysis.
• Feature Engineering: Create features that can indicate fraudulent behaviour, such as transaction frequency or amount.
• Establish Detection Model: Build a model which will be able to detect fraud using machine learning algorithms.
• Configure Real-Time Processing: Create an event streaming architecture which employs Kafka or Spark Streaming to provide timely transaction processing.
• Integrate Model: Deploy the model within the streaming pipeline to evaluate transactions on the fly.
• Alert System: Develop a notification system to alert when potential fraud is detected.

Tech Stack

• Programming Language: Python, Java
• Streaming Tools: Apache Kafka, Apache Spark Streaming
• Machine Learning Libraries: Scikit-learn, TensorFlow
• Database: PostgreSQL, MongoDB
• Notification Tools: Twilio API, Email Services

Skills Developed

• Real-time data streaming and processing
• Building and deploying machine learning models
• Feature engineering for fraud detection
• Integrating models with streaming pipelines
• Implementing alert and notification systems

Data Pipeline Using Airflow

Creating a Data Pipeline Using Airflow allows you to orchestrate complex workflows and automate data processing tasks.

Steps to Complete the Project

• Install Airflow: Set up Apache Airflow on your local machine or a server.
• Define DAGs: Create Directed Acyclic Graphs (DAGs) to represent your workflow.
• Add Tasks: Implement tasks for data extraction, transformation, and loading using Python operators.
• Configure Dependencies: Set task dependencies to ensure the correct execution order.
• Set Up Scheduling: Schedule your DAGs to run at specific intervals or triggers.
• Monitor Pipelines: Use Airflow’s UI to track pipeline execution and troubleshoot issues.
• Optimize Workflows: Refine DAGs for better performance and reliability.

Tech Stack

• Orchestration Tool: Apache Airflow
• Programming Language: Python
• ETL Tools: Python scripts, SQL
• Database: PostgreSQL, MySQL
• Monitoring Tools: Airflow UI, Prometheus, Grafana

Skills Developed

• Designing and managing workflows with Airflow
• Automating ETL processes
• Scheduling and monitoring data pipelines
• Troubleshooting and optimizing pipeline performance

Conclusion

Working on real data engineering projects is wonderful for enhancing your abilities as well as gaining first-hand experience. These projects enable one to gain knowledge on how data is managed, starting from its collection, storage, processing and all the way to analysis of the data. By engaging in these projects, it is possible to build a portfolio that potential employers will find attractive. Learn more about data engineering with the Accelerator Program in Business Analytics and Data Science with Nasscom by Hero Vired and also get a professional certificate.

Irrespective of whether you are just fresher or have some experience, there are projects that would suit your level. When actively participating in these projects, one prepares oneself for real-life scenarios as well as improving one’s chances for advancement in the data engineering profession. Get started with your expertise today and take your data engineering experience to new heights!