This project demonstrates how to use Dask SQL to execute and analyze TPC-H benchmark queries for distributed data processing and performance evaluation.
The notebook illustrates how Dask SQL enables scalable analytics by combining the flexibility of Python with the power of SQL for large datasets.
- Execution of TPC-H benchmark queries (Q1–Q22) using Dask SQL
- Comparison between Dask SQL and traditional SQL engines
- Exploration of distributed computing concepts
- Performance metrics and query optimization techniques
Before running the notebook, install the following dependencies:
pip install dask dask-sql pandas numpy matplotlib jupyter📂 Project Structure
RanaDaskSQL_tpch.ipynb # Main notebook containing code and analysis
README.md- Open the notebook:
jupyter notebook RanaDaskSQL_tpch.ipynb-
Run all cells in sequence to:
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Initialize the Dask SQL context
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Load TPC-H tables (e.g., lineitem, orders, customer, etc.)
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Execute benchmark queries
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Visualize query results and performance
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Query execution times for multiple datasets
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Result previews for benchmark queries
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Comparative visualizations of distributed vs. local execution
This project is for educational purposes only.