Data Engineering Zoomcamp

My Journey Through BigQuery: Lessons from Data Engineering Zoomcamp Module 3

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Dinesh
·

3 min read

As I wrap up Module 3 of the Data Engineering Zoomcamp, I want to share my experience working with Google BigQuery and the valuable insights I gained while analyzing the NYC Yellow Taxi dataset. This module has fundamentally changed how I think about data warehousing and query optimization.

Understanding Table Types in BigQuery

One of the most enlightening aspects was learning the difference between External and Materialized tables. Working with the NYC taxi data, I created both types:

  • External Tables: These act as a window to data stored in GCS buckets. I found it fascinating how they let you query data without actually loading it into BigQuery, saving on storage costs. The catch? Query performance might be slightly slower.

  • Materialized Tables: These are your traditional BigQuery tables where data is stored within BigQuery itself. While they use more storage, they often provide faster query performance.

The Power of Columnar Storage

A lightbulb moment came when I ran queries selecting different columns. BigQuery's columnar storage means it only reads the columns you actually need. When I queried:

SELECT PULocationID FROM yellow_taxi_materialized;

versus

SELECT PULocationID, DOLocationID FROM yellow_taxi_materialized;

The estimated bytes processed doubled! This really drove home why columnar storage is so powerful for analytical queries.

Optimization Techniques

The most practical skills I gained were around table optimization. I learned that:

  1. Partitioning is crucial when you frequently filter on a specific column (like dates)

  2. Clustering helps when you often sort or filter by certain columns

  3. But - and this was key - you shouldn't always cluster your tables. It's not a one-size-fits-all solution

Working with the taxi data, I created a table partitioned by dropoff datetime and clustered by VendorID. The performance improvement was dramatic - queries that previously scanned 310MB now only needed to look at 26MB!

Cost Optimization

Perhaps the most practical lesson was about cost optimization. I learned to:

  • Check estimated bytes before running queries

  • Use column selection wisely instead of SELECT *

  • Leverage table metadata for simple operations (like COUNT(*))

  • Choose between external and materialized tables based on access patterns

Surprising Discoveries

One fascinating discovery was when running a simple COUNT(*) query returned 0 bytes processed. This taught me how BigQuery optimizes even simple operations by storing metadata about the table.

Real-World Application

Working with the NYC Yellow Taxi dataset wasn't just about learning BigQuery features - it was about solving real-world problems. The dataset, with its 20+ million records, provided a perfect playground for understanding how these concepts apply at scale.

Takeaways for Future Projects

As I move forward, I'll be:

  1. Always considering partitioning strategy before creating large tables

  2. Being mindful of query costs and optimization techniques

  3. Using external tables for data that doesn't need frequent access

  4. Thinking carefully about column selection in queries

Conclusion

Module 3 has equipped me with practical skills in data warehousing that go beyond just writing queries. It's about understanding how data is stored, accessed, and optimized. These insights will be invaluable as I continue my journey in data engineering.

The beauty of BigQuery lies not just in its power to handle massive datasets, but in how it allows you to optimize and fine-tune based on your specific needs. As data continues to grow in volume and importance, these skills become increasingly crucial for any data professional.

#DataEngineering #BigQuery #DEZoomcamp #DataWarehouse #TechLearning

Remember to keep learning and experimenting - there's always more to discover in the world of data engineering!

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