Data Warehousing with BigQuery

Over the last week, I’ve had the opportunity to dive deep into data warehousing using BigQuery as part of the third module in the Data Engineering Zoomcamp @DataTalks.Club. This journey has not only expanded my technical knowledge but also reshaped my approach to designing scalable, efficient data architectures. In this post, I’ll share my key learnings, challenges, and best practices for leveraging BigQuery in modern data warehousing.


Getting Started with BigQuery

BigQuery is Google Cloud’s serverless, highly scalable data warehouse that lets you run SQL queries on massive datasets in seconds. One of its core advantages is that it abstracts away the infrastructure—meaning you focus on the data and the insights, while Google handles scalability, performance, and maintenance.


Why BigQuery?

  • Serverless Architecture: No need to manage hardware or tune clusters.
  • Columnar Storage: Only the necessary columns are scanned, making queries both cost-effective and lightning fast.
  • Integrated Ecosystem: Seamlessly integrates with other Google Cloud services like Cloud Storage, Dataflow, and Looker.
I began my journey by exploring how to load data into BigQuery using both external tables and native tables.


Loading Data: External vs. Native Tables

For this module, I worked with the Yellow Taxi Trip Records for the first half of 2024. I uploaded six Parquet files (one for each month) to a Google Cloud Storage (GCS) bucket named liquid-verbena-448913-p5-terra-bucket.


Creating an External Table

Using the BigQuery UI, I created an external table that references these Parquet files. The SQL looked like this:


BigQuery automatically concatenates the files vertically as long as they share the same schema. This method is excellent for ad-hoc querying without incurring storage costs for duplicated data.


Creating a Native Table

Since the homework required a table in BigQuery (not just an external table), I loaded the data into a native table with:


This step physically loads the data into BigQuery’s native storage, allowing me to run more optimized queries and perform operations that are not supported on external tables.



Querying and Optimizing Data

Once the data was in BigQuery, I experimented with several queries. For example, to count the distinct number of pickup locations, I ran:


BigQuery’s columnar storage means that if I query just one column, only that column is scanned—helping keep costs down. However, when I expanded my query to retrieve both PULocationID and DOLocationID, the estimated bytes processed increased, simply because BigQuery had to read additional data.


Optimizing Table Structure

One of the most exciting parts of this learning module was understanding how table design impacts performance. My queries always filtered on tpep_dropoff_datetime and ordered results by VendorID. The optimal strategy turned out to be:

  • Partition by tpep_dropoff_datetime: This ensures that queries filtering by drop-off time scan only the relevant partitions.
  • Cluster on VendorID: Clustering organizes data within partitions by VendorID, which speeds up sorting and filtering.
The new table creation looked like this:


This design greatly reduced query execution times and lowered costs by minimizing the amount of data scanned during operations.


Reflections on the Data Engineering Zoomcamp

Participating in the Data Engineering Zoomcamp @DataTalks.Club has been a game-changer for me. Not only did I learn about BigQuery’s robust capabilities, but I also gained hands-on experience with:

  • Data ingestion and transformation pipelines
  • Query optimization techniques
  • Cost management strategies in a cloud data warehouse
Each module of the course has challenged me to think critically about data architecture, performance, and scalability. The practical exercises—like loading large datasets, creating external and native tables, and optimizing queries with partitioning and clustering—have been invaluable in translating theoretical concepts into real-world applications.



Final Thoughts

Data warehousing with BigQuery is about more than just running SQL queries on huge datasets—it’s about leveraging cloud-native features to build efficient, scalable, and cost-effective data solutions. Whether you’re new to the world of data engineering or looking to refine your skills, BigQuery offers a powerful platform that can grow with your needs.

I’m excited to continue exploring these technologies and look forward to sharing more insights from my ongoing journey in data engineering. If you have any questions or want to discuss best practices, feel free to leave a comment or reach out!

Happy querying!



This post captures my learning and experiences with BigQuery during the Zoomcamp. I hope it helps you understand how thoughtful table design and query optimization can significantly improve data processing performance in a cloud data warehouse.


Comments

Post a Comment

Popular posts from this blog

My midterm project at MLZoomcamp led by Alexey Grigorov for DataTalksClub

 Predicting Patient No-Shows: A Data-Driven Approach Hospital no-shows significantly disrupt healthcare systems, wasting resources and delaying care for those in need. My midterm project for the MLZoomcamp , led by Alexey Grigorev and hosted by DataTalksClub, tackles this challenge using machine learning to predict no-show probabilities for appointments in Brazilian hospitals. Here's how I approached the problem: The Challenge The dataset, sourced from Kaggle, includes over 110,000 appointments and diverse features such as patient demographics, appointment details, and medical history. However, achieving reliable predictions is complex due to: Imbalanced Data : About 80% of appointments were attended, while 20% were no-shows. Dependence on Feature Engineering : Key predictors like patient history (previous/missed appointments) were engineered from the raw data. Bias Mitigation : Socioeconomic factors, such as neighborhood, required careful handling to ensure fairness. The Solution ...
Read more

Logistic Regression: A walkthrough by Alexey Grigorev

Logistic Regression is one of the foundational algorithms for classification tasks, and Alexey Grigorev at DataTalks.Club provides an insightful approach to understanding it in his courses, such as the Machine Learning Zoomcamp. Alexey Grigorev provides a clear and practical explanation of Logistic Regression, helping learners understand its application and limitations in real-world scenarios. In his teaching, Alexey emphasizes the simplicity and interpretability of Logistic Regression. The model predicts the probability that a given input belongs to a particular class, which makes it especially useful in binary classification problems. For example, it’s widely applied in fields like customer churn prediction, fraud detection, and medical diagnoses. Logistic Regression works by modeling the relationship between input features and the probability of a binary outcome using a sigmoid function. This makes the predictions constrained between 0 and 1. Alexey often stresses the importance of ...
Read more

Starting my Data Engineering journey with a foundational insight on Docker, Terraform and Google Cloud Platform

 The Data Engineering Zoomcamp 2025, led by Alexey Grigorev at DataTalksClub, offers an in-depth exploration of modern data engineering practices. The first module, "Containerization and Infrastructure as Code," serves as a foundational entry point into the course, equipping participants with essential skills for building and managing scalable data systems. Module 1: Containerization and Infrastructure as Code This module introduces participants to two pivotal concepts in data engineering: containerization and infrastructure as code (IaC). By leveraging these technologies, data engineers can create consistent, reproducible environments and automate the provisioning of infrastructure, leading to more efficient and reliable data pipelines. Key Topics Covered: Introduction to Google Cloud Platform (GCP): Participants are introduced to GCP, a leading cloud service provider offering a suite of tools and services for building and managing data systems. The course provides guidance ...
Read more