Building My First Real-Time Dashboard with ClickHouse and Streamlit: TrendLite Breakdown

Hey folks! I'm Hussain — a Computer Science grad and current Associate Data Engineer Intern at Quantrail. Lately, I’ve been diving deep into the world of data infrastructure and OLAP systems. Today, I want to walk you through my experience building TrendLite — a real-time sales analytics dashboard powered by ClickHouse.

🧐 What Is TrendLite?

TrendLite is a retail analytics dashboard designed to visualize product demand, top-performing categories, and sales trends — in real time. The goal? Take a raw CSV of sales data and turn it into a fast, interactive dashboard using scalable data infrastructure.

🛠️ Tech Stack

• ClickHouse → Blazing-fast OLAP database for analytics
• Streamlit → Frontend for live data visualization
• Python & Pandas → Data preprocessing and transformation

⚡ Why ClickHouse?

While working with traditional databases, I noticed performance issues when running analytical queries on large datasets. That’s when I discovered ClickHouse, an open-source OLAP DBMS built for speed.

After completing the "Learn ClickHouse – A Fast Open-Source OLAP DBMS" course on Udemy, I decided to put my knowledge into practice — and TrendLite was born.

⚙️ How I Built It – Step by Step

1. Data Preprocessing

• Cleaned and formatted the raw sales data using Pandas
• Handled missing values, date formats, and basic transformations

2. ClickHouse Setup

• Installed and configured ClickHouse locally
• Loaded the cleaned data into ClickHouse tables
• Wrote optimized queries like:

  SELECT product, SUM(quantity) AS total_sales
  FROM retail_data
  GROUP BY product
  ORDER BY total_sales DESC
  LIMIT 10;

3. Dashboard with Streamlit

• Built an interactive frontend using Streamlit

• Visualized key metrics:

  • 🏆 Top products by total sales
  • 📊 Revenue trends over time
  • 🧾 Category-wise breakdowns

📸 Sneak Peek

Want to explore the dashboard in action?
👉 Check out the GitHub Repository

The repo includes the Streamlit app code, ClickHouse integration, and sample data.

🧠 Key Takeaways

• Learned the OLAP vs OLTP difference and their use cases
• Understood how ClickHouse executes large-scale queries efficiently
• Got hands-on with basic ETL concepts and data modeling
• Built a clean UI for data storytelling using Streamlit
• Faced (and fixed!) real-world bugs and performance issues 💥

📌 What’s Next?

I plan to:

• Experiment with streaming data ingestion (Kafka might be next!)
• Open-source the project with deployment instructions
• Publish a Part 2 walkthrough on the code and architecture

💬 Final Thoughts

Working on TrendLite made me appreciate the power of OLAP systems like ClickHouse — especially when paired with lightweight tools like Streamlit. If you're starting out in data engineering or analytics, building a project like this is a great way to learn fast, hands-on.

Feel free to connect — I'm always open to sharing ideas, learning together, or just nerding out about data 🙌