Summary  
This chapter covers strategies for estimating and controlling costs in usage-based data query and storage services, detailing pricing models, storage tiers, partitioning techniques, and billing alerts.

General domain of usage  
Cloud data analytics

BigQuery storage is divided into two types: **Active Storage** and **Long-term Storage**. This model is designed to balance performance and cost without requiring manual data movement.

**Active Storage** is used for data that has been accessed within the last 90 days. It supports frequent querying and is well suited for real-time and operational analytics.

**Long-term Storage** applies automatically to tables that have not been queried for more than 90 days. Data remains fully available, but storage costs are reduced without any changes to queries or table structure.

BigQuery optimizes storage through built-in features such as **automatic compression**, **geographic redundancy**, and **flexible data lifecycle management**. There is no minimum storage duration, allowing data to move between storage tiers seamlessly based on usage.

To control storage costs, common best practices include:

* Deleting unused tables;
* Setting table expiration dates;
* Partitioning large tables to limit scanned data;
* Monitoring storage usage regularly.

BigQuery also provides several **free storage-related operations**, including cached query results, loading data into tables, modifying schemas, and creating tables using `SELECT` statements. These features help reduce operational overhead while keeping storage efficient.

How does Long-term Storage help reduce costs?

Which of the following is a cost-saving method in BigQuery?

Dive into the fundamentals of the BigQuery platform and transform the way you handle cloud data. Master the skills needed to navigate the console, manage projects, and optimize query costs. Get hands-on experience with BigQuery-specific features like array operations, geographic data, and date functions. Explore advanced optimization techniques and learn the secrets of building machine learning models directly within the warehouse using BigQuery ML.

Gain a solid understanding of the BigQuery environment. distinct from traditional databases. Learn to navigate the console, manage projects and datasets, and analyze costs effectively. Build confidence in setting up your workspace for efficient data analysis.

Explore the essentials of the Query Engine to retrieve data efficiently. Master data selection techniques and learn how to analyze specific product metrics. Discover performance features and query testing methods to ensure your analysis is both fast and accurate.

Take your knowledge to the next level by mastering BigQuery’s unique data types. Learn to handle complex array operations and manipulate date and time functions. Explore geographic features to solve location-based challenges like calculating city distances.

Optimize your workflow by managing large tables and improving query performance. Learn the secrets of effective error handling to troubleshoot issues quickly. Master the skills needed to maintain a high-performance database environment.

Apply your knowledge to real-world scenarios. Discover how to build comprehensive reports and integrate BigQuery with external tools. Transform raw data into actionable business insights through practical application and reporting strategies.

Dive into the fundamentals of BigQuery ML. Discover how to create and execute machine learning models using standard SQL without moving data. Gain hands-on experience with model walkthroughs to predict outcomes directly within the platform.

Understanding BigQuery Costs

1. What defines Active Storage in BigQuery?

2. How does Long-term Storage help reduce costs?

3. Which of the following is a cost-saving method in BigQuery?

4. What is one free operation in BigQuery?