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deepmind.google·6d ago
AI Engineering
Lobsters·Jun 23·5 min read
Implementing a Custom Query Language with Python and Apache Spark
TL;DR
Analysts needed a way to query vehicle maintenance data without deep technical knowledge. A custom query language, Entity History Query Language (EHQL), was built using Python and Apache Spark to simplify this process.
✦ Why It Matters
Engineers can create user-friendly query languages to empower non-technical users in data analysis.
Key Takeaways
- Use Python for building custom query languages.
- Leverage Apache Spark for efficient data processing.
- Design user-friendly syntax for non-technical users.
- Focus on compiling custom queries into executable formats.
Full Summary
Vehicle maintenance data often requires complex queries that can be challenging for non-technical analysts. To address this, a custom query language called Entity History Query Language (EHQL) was developed using Python, a popular programming language for data processing, and Apache Spark, a powerful analytics engine.
The implementation focuses on compiling EHQL queries into executable Spark queries, leveraging existing data stored in Parquet format. A sample query was created to find vehicles that had both an oil change and a transmission fluid change in their maintenance history.
This approach significantly reduces the need for analysts to understand intricate query syntax while still providing powerful data retrieval capabilities. The results demonstrate that non-technical users can effectively access and analyze vehicle maintenance data, leading to better decision-making.
Overall, this implementation showcases how custom languages can bridge the gap between technical complexity and user accessibility.