# BigQuery for Data Engineering (Data Engineering)

> Serverless architecture, partitioning, clustering, costs, UDFs, federated queries, scheduled queries, materialized views

- 20 interview questions
- Mid-Level
- [Interview Questions: Data Engineering](https://sharpskill.dev/en/technologies/data-engineering/interview-questions.md)

## 1. What storage architecture does BigQuery use?

**Answer**

BigQuery uses a serverless architecture with columnar storage called Capacitor. This architecture separates storage and compute, enabling independent scaling and separate billing. Columnar storage is optimized for analytical queries as it allows reading only the required columns, significantly reducing I/O.

## 2. What is the main advantage of table partitioning in BigQuery?

**Answer**

Partitioning divides a large table into smaller segments based on a column (usually a date). During queries, BigQuery can skip irrelevant partitions (partition pruning), reducing the amount of data scanned. This improves performance and reduces costs since BigQuery charges based on the volume of data processed.

## 3. What types of partitioning are available in BigQuery?

**Answer**

BigQuery supports three partitioning types: by DATE or TIMESTAMP column (most common), by integer range (INTEGER RANGE), and by ingestion time (_PARTITIONTIME). Date partitioning is recommended for time-series data as it enables efficient partition pruning on date filters.

## 17 more questions available

- What is the difference between partitioning and clustering in BigQuery?
- How to optimize query costs in BigQuery?

Sign up for free: https://sharpskill.dev/en/login

## Other Data Engineering interview topics

- [Linux & Shell - Fundamentals](https://sharpskill.dev/en/technologies/data-engineering/interview-questions/linux-shell-basics.md): 20 questions, Junior
- [Git & GitHub - Fundamentals](https://sharpskill.dev/en/technologies/data-engineering/interview-questions/git-github-fundamentals.md): 20 questions, Junior
- [Advanced Python for Data Engineering](https://sharpskill.dev/en/technologies/data-engineering/interview-questions/python-advanced-de.md): 25 questions, Junior
- [Docker - Fundamentals](https://sharpskill.dev/en/technologies/data-engineering/interview-questions/docker-fundamentals.md): 25 questions, Junior
- [Google Cloud Platform - Fundamentals](https://sharpskill.dev/en/technologies/data-engineering/interview-questions/gcp-fundamentals.md): 20 questions, Junior
- [CI/CD and Code Quality](https://sharpskill.dev/en/technologies/data-engineering/interview-questions/ci-cd-code-quality.md): 20 questions, Mid-Level
- [Docker Compose](https://sharpskill.dev/en/technologies/data-engineering/interview-questions/docker-compose.md): 20 questions, Mid-Level
- [FastAPI - Data APIs](https://sharpskill.dev/en/technologies/data-engineering/interview-questions/fastapi.md): 20 questions, Mid-Level
- [Advanced SQL for Data Engineering](https://sharpskill.dev/en/technologies/data-engineering/interview-questions/sql-advanced-de.md): 20 questions, Mid-Level
- [Data Lake - Architecture and Ingestion](https://sharpskill.dev/en/technologies/data-engineering/interview-questions/data-lake.md): 20 questions, Mid-Level
- [PostgreSQL - Administration](https://sharpskill.dev/en/technologies/data-engineering/interview-questions/postgresql-admin.md): 20 questions, Mid-Level
- [Data Modeling for Data Engineering](https://sharpskill.dev/en/technologies/data-engineering/interview-questions/data-modeling-de.md): 20 questions, Mid-Level
- [Fivetran & Airbyte - Data Ingestion](https://sharpskill.dev/en/technologies/data-engineering/interview-questions/fivetran-airbyte.md): 20 questions, Mid-Level
- [dbt - Fundamentals](https://sharpskill.dev/en/technologies/data-engineering/interview-questions/dbt-fundamentals-de.md): 20 questions, Mid-Level
- [Apache Airflow - Fundamentals](https://sharpskill.dev/en/technologies/data-engineering/interview-questions/airflow-fundamentals.md): 20 questions, Mid-Level
- [Kubernetes - Fundamentals](https://sharpskill.dev/en/technologies/data-engineering/interview-questions/kubernetes-fundamentals.md): 20 questions, Mid-Level
- [dbt - Advanced Features](https://sharpskill.dev/en/technologies/data-engineering/interview-questions/dbt-advanced-de.md): 20 questions, Senior
- [ETL / ELT / ETLT Patterns](https://sharpskill.dev/en/technologies/data-engineering/interview-questions/etl-elt-patterns.md): 20 questions, Senior
- [Apache Airflow - Advanced](https://sharpskill.dev/en/technologies/data-engineering/interview-questions/airflow-advanced.md): 20 questions, Senior
- [Airflow + dbt - Pipeline Orchestration](https://sharpskill.dev/en/technologies/data-engineering/interview-questions/airflow-dbt-integration.md): 20 questions, Senior
- [PySpark - Large-Scale Processing](https://sharpskill.dev/en/technologies/data-engineering/interview-questions/pyspark.md): 20 questions, Senior
- [Google Pub/Sub - Data Streaming](https://sharpskill.dev/en/technologies/data-engineering/interview-questions/pubsub-streaming.md): 20 questions, Senior
- [Apache Beam & Dataflow](https://sharpskill.dev/en/technologies/data-engineering/interview-questions/apache-beam-dataflow.md): 20 questions, Senior
- [Kubernetes - Production and Scaling](https://sharpskill.dev/en/technologies/data-engineering/interview-questions/kubernetes-advanced.md): 20 questions, Senior
- [Terraform - Infrastructure as Code](https://sharpskill.dev/en/technologies/data-engineering/interview-questions/terraform.md): 20 questions, Senior
- [NoSQL Databases](https://sharpskill.dev/en/technologies/data-engineering/interview-questions/nosql-databases.md): 20 questions, Senior
- [Modern Data Architecture](https://sharpskill.dev/en/technologies/data-engineering/interview-questions/data-architecture.md): 20 questions, Senior
- [Monitoring and Observability](https://sharpskill.dev/en/technologies/data-engineering/interview-questions/monitoring-observability.md): 20 questions, Senior
- [IAM and Data Security](https://sharpskill.dev/en/technologies/data-engineering/interview-questions/iam-security-de.md): 20 questions, Senior

---

Source: SharpSkill (https://sharpskill.dev), tech interview preparation for your real stack.
HTML version of this page: https://sharpskill.dev/en/technologies/data-engineering/interview-questions/bigquery-de