Skills Covered: Web Scraping, SQL, API Development

Tech Stack: Python (Scrapy/BeautifulSoup), SQLite/PostgreSQL

3. Movie Recommendation System using SQL

Building a movie recommendation system based on movie ratings using SQL queries.

Key Steps

• Collecting and cleaning movie rating data
• Designing a database schema in PostgreSQL
• Writing SQL queries to recommend movies based on user ratings

Skills Covered: SQL Query Optimization, Data Warehousing

Tech Stack: PostgreSQL, Python (Pandas, Scikit-learn)

4. Data Pipeline for Real-Time Stock Prices

To build a data pipeline that collects real-time stock price data from a public API. Store the data in a PostgreSQL database and process it using Pandas for trend analysis.

Key Steps

• Fetching stock price data using Python requests
• Processing and cleaning the data
• Storing data in PostgreSQL
• Visualizing trends using Matplotlib

Skills Covered: API Integration, Streaming Data, SQL, Pandas

Tech Stack: Python, Kafka, PostgreSQL, Pandas

5. E-Commerce Customer Segmentation

To segment customers based on purchase behavior using clustering algorithms like K-Means.

Key Steps

• Extracting customer transaction data
• Cleaning and preprocessing data
• Applying K-Means clustering and visualizing customer segments

Skills Covered: Data Preprocessing, Clustering, Machine Learning

Tech Stack: Python (Scikit-learn, Pandas), MySQL

6. ETL Pipeline for Sales Data

Creating an ETL pipeline to extract sales data from CSV files and transform it by removing the duplicates and standardizing format. Loads the data into a MySQL database and visualizes it using Power BI.

Key Steps

• Automating ETL using Apache Airflow
• Cleaning and transforming data using Pandas
• Storing the cleaned data in MySQL
• Creating sales dashboards in Power BI

Skills Covered: Extract-Transform-Load (ETL), SQL, Data Cleaning

Tech Stack: Apache Airflow, MySQL, Pandas, Power BI

7. Building a Data Lake on Azure

Setting up an Azure Data Lake Gen2 to store unstructured and structured data from multiple sources. To process and move data, create an Azure Data Factory pipeline.

Key Steps

• Creating an Azure Data Lake Storage account
• Storing raw and processed data in different layers
• Using Azure Data Factory to move and transform data

Skills Covered: Cloud Data Storage, Azure Data Lake, Big Data Processing

Tech Stack: Azure Data Lake Gen2, Azure Data Factory

8. Twitter Sentiment Analysis Pipeline

Building a pipeline that fetches tweets, performs sentiment analysis, and stores results in a MongoDB database.

Key Steps

• Collecting tweets using the Twitter API
• Performing sentiment analysis using NLTK or TextBlob
• Storing results in MongoDB andVisualizing sentiment trends

Skills Covered: Text Processing, NLP, Sentiment Analysis

Tech Stack: Twitter API, Python, MongoDB, Apache Spark

9. Data Pipeline for IoT Sensor Data

Processing IoT sensor data using Kafka and storing it in InfluxDB for real-time monitoring.

Key Steps

• Collect sensor data and push it to Kafka
• Store processed data in InfluxDB
• Create real-time dashboards in Grafana

Skills Covered: Time-Series Data, Big Data Processing, Kafka

Tech Stack: Apache Kafka, InfluxDB, Grafana

10. Automating Data Reports with Python

To automate the generation of business reports from Excel data using Python.

Key Steps

• Reading and processing Excel data using Pandas
• Generating summary reports automatically
• Scheduling reports using a cron job

Skills Covered: Data Automation, Excel Reporting, Scheduling

Tech Stack: Python, Pandas, OpenPyXL, Scheduler (cron)

Conclusion

Finally, These data engineering projects cover the essential data engineering concepts like ETL, cloud storage and automation. To master the skills of data engineering, join our data engineering training in Chennai using professional trainers. By working on these projects, gain practical experience and well-prepare yourself for real-world data engineering roles.