# BigQuery - Fundamentals (Data Analytics)

> BigQuery architecture, datasets, tables, schemas, data types, console, costs

- 20 interview questions
- Junior
- [Interview Questions: Data Analytics](https://sharpskill.dev/en/technologies/data-analytics/interview-questions.md)

## 1. What is BigQuery?

**Answer**

BigQuery is Google Cloud Platform's fully managed, serverless data warehouse. It allows running analytical SQL queries on very large volumes of data (petabytes) without managing any infrastructure. Its architecture separates storage and compute, enabling each component to scale independently based on needs.

## 2. What is the main characteristic of BigQuery's architecture?

**Answer**

BigQuery is built on an architecture that separates storage (Colossus) and compute (Dremel). This separation allows storing massive amounts of data at low cost while dynamically allocating the necessary compute power only when queries are executed. This avoids provisioning and maintaining fixed clusters.

## 3. What is a dataset in BigQuery?

**Answer**

A dataset in BigQuery is a logical container that groups tables, views, and functions. It plays a role similar to a schema in traditional databases. Each dataset is associated with a Google Cloud project and has its own geographic location settings and access control.

## 17 more questions available

- What is the organizational hierarchy of resources in BigQuery?
- How to reference a table in a BigQuery query?

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

## Other Data Analytics interview topics

- [Google Sheets - Fundamentals](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/google-sheets-basics.md): 20 questions, Junior
- [Google Sheets - Advanced Formulas](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/google-sheets-advanced.md): 20 questions, Junior
- [SQL - Fundamentals](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/sql-basics.md): 25 questions, Junior
- [SQL - Aggregations and Grouping](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/sql-aggregations.md): 20 questions, Junior
- [SQL - Joins](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/sql-joins.md): 20 questions, Junior
- [Data Cleaning](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/data-cleaning.md): 20 questions, Junior
- [KPIs and Business Metrics](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/kpis-metrics.md): 20 questions, Junior
- [Descriptive Statistics](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/descriptive-statistics.md): 20 questions, Junior
- [Zapier and No-Code Automation](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/zapier-automation.md): 20 questions, Junior
- [Data Visualization Principles](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/data-viz-principles.md): 20 questions, Junior
- [Python & Pandas - Fundamentals](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/python-pandas-basics.md): 20 questions, Junior
- [Google Sheets - Automated Dashboards](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/google-sheets-dashboards.md): 20 questions, Mid-Level
- [SQL - Subqueries and CTEs](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/sql-subqueries-ctes.md): 20 questions, Mid-Level
- [SQL - Window Functions](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/sql-window-functions.md): 20 questions, Mid-Level
- [BigQuery - Advanced Features](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/bigquery-advanced.md): 20 questions, Mid-Level
- [Data Modeling](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/data-modeling.md): 20 questions, Mid-Level
- [Funnel and Conversion Analysis](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/funnel-analysis.md): 20 questions, Mid-Level
- [Cohort and Retention Analysis](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/cohort-retention.md): 20 questions, Mid-Level
- [Google Tag Manager and Tracking](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/gtm-tracking.md): 20 questions, Mid-Level
- [APIs and Webhooks](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/apis-webhooks.md): 20 questions, Mid-Level
- [dbt - Fundamentals](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/dbt-fundamentals.md): 20 questions, Mid-Level
- [AB Testing and Applied Statistics](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/ab-testing.md): 20 questions, Mid-Level
- [Looker Studio (Google Data Studio)](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/looker-studio.md): 20 questions, Mid-Level
- [Power BI - Fundamentals](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/power-bi-fundamentals.md): 20 questions, Mid-Level
- [SQL - Advanced Analytical Queries](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/sql-advanced-analytics.md): 20 questions, Senior
- [dbt - Advanced Features](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/dbt-advanced.md): 20 questions, Senior
- [Power BI - DAX and Advanced Dashboards](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/power-bi-dax.md): 20 questions, Senior
- [Python Analytics - Advanced Analysis and ML](https://sharpskill.dev/en/technologies/data-analytics/interview-questions/python-analytics-advanced.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-analytics/interview-questions/bigquery-fundamentals