Understanding BigQuery Pricing: A Complete Cost Prediction Guide

Google BigQuery is a powerful tool for handling large datasets, and understanding its pricing structure is crucial, as well as empowering for managing costs. BigQuery pricing is influenced by several factors, primarily storage and processing costs. Storage costs depend on the amount of data stored and how often it is accessed, while processing costs are based on the amount of data processed during queries.

In this guide, we'll break down these key pricing factors and offer strategies for optimizing your BigQuery expenses. Whether you're new to BigQuery or looking to refine your cost management practices, this article will provide practical insights to help you navigate its pricing structure effectively and make informed decisions.

Several key factors influence Google BigQuery pricing, including storage costs, analysis costs, data types, and the volume of data processed. Understanding these elements can help you better plan and manage your budget.

Storage costs significantly influence BigQuery pricing. Data storage in BigQuery is divided into active and long-term storage, each with distinct pricing models. Active storage is pricier due to higher access frequency, while long-term storage offers cost savings for infrequently accessed data. Effective management of storage types helps optimize overall expenditure and ensures efficient data handling.

Processing costs incurred during query execution constitute a significant factor in BigQuery pricing. These costs are calculated based on the quantum of data processed by each query. Optimizing query efficiency and reducing the data scanned can significantly lower these expenses, making it crucial to design streamlined queries and leverage BigQuery's cost-control features.

BigQuery storage costs are divided into active and long-term storage, each with its distinct pricing model. Knowing these categories helps you manage your data storage efficiently and cost-effectively.

Frequently accessed data incurs active storage costs in BigQuery. This type of storage is essential for datasets requiring regular updates and analyses. Priced higher due to increased resource demand, the active storage ensures fast data retrieval and processing, supporting real-time analytics and operational efficiency. Proper management of active storage helps balance performance and costs.

Long-term storage costs in BigQuery offer a cost-effective solution for infrequently accessed data. This storage type is ideal for archiving historical data, providing significant savings compared to active storage. Long-term storage ensures data retention for compliance and historical analysis while minimizing expenses, making it a strategic and prudent choice for managing large datasets over extended periods.

Data storage costs in BigQuery vary by data type and storage duration. Logical storage refers to the actual data volume, excluding any redundant or padded data, while physical storage includes the actual physical space the data utilizes, including any compression or indexing overhead. Understanding these differences helps optimize data management and control expenses effectively.

You're billed based on the volume of data you store in BigQuery. To calculate your total data usage, you need to understand the byte size of each column's data type. Here are the sizes for BigQuery data types:

For BigQuery, the cost per 1 GiB varies depending on storage type. Active logical storage incurs higher charges due to frequent access, while long-term logical storage is more economical for infrequently accessed data. 

Active physical storage includes additional overhead costs, whereas long-term physical storage provides a cost-effective solution for managing large datasets over extended periods.

Active logical storage in BigQuery calculates costs based on the logical size of the data. This method ensures efficient use of storage space by focusing on the actual data volume, excluding redundant or padded data, thus providing a cost-effective solution for frequently accessed datasets.

To illustrate, keeping a 100 GiB table in active storage for one month costs $0.02 per GiB, totaling $2 (100 GiB x $0.02). Including the free 10 GiB, you get 110 GiB for $2.

In BigQuery, active physical storage refers to the actual physical space the data utilizes, including any compression or indexing overhead. Costs are based on stored raw bytes, ensuring precise pricing for high-demand data that is frequently accessed and queried.

Active physical storage costs $0.04 per GiB each month. This option also includes a free tier, where the first 10 GiB are provided at no charge every month. This means if you have 50 GiB of data in active physical storage, you would only be billed for 40 GiB, resulting in a monthly cost of (40 x 0.04) = $1.60.

Long-term logical storage offers a cost-efficient solution for data that doesn't require frequent access. This type of storage is calculated based on the logical size of the data, making it ideal for archiving large volumes of data at a lower cost.

For example, storing a 200 GiB table for one-month costs (200 x 0.01) = $2. This lower cost makes it an attractive option for data that is not frequently accessed. However, if the table is updated, it is reclassified as active storage, and the 90-day period resets from the beginning. 

The first 10 GiB of long-term logical storage is free each month, further reducing the cost for users with smaller datasets.

Long-term physical storage in BigQuery is designed for infrequently accessed data. It offers reduced costs compared to active storage and is calculated based on the physical space used, making it an economical choice for long-term data retention.

Long-term physical storage costs $0.02 per GiB each month, making it a cost-effective option for storing data that is rarely accessed but still needs to be retained. Similar to long-term logical storage, this option also benefits from a free tier, offering the first 10 GiB of storage at no cost each month. If the stored data is updated, it transitions to active physical storage, and the 90-day period starts over.

Storage costs also vary by location. For instance, Sydney (Australia-southeast1) charges $0.025 per GiB, whereas Singapore (Asia-southeast1) and Tokyo (Asia-northeast1) cost $0.022 per GiB. Understanding these location-based differences can help you optimize your BigQuery storage strategy to fit your budget and performance requirements.

With these rates, you can optimize your BigQuery storage strategy to fit your budget and performance requirements.

Calculations for BigQuery cost per 1 TB follow similar principles. Active storage is pricier due to higher resource demand and frequent access. In contrast, long-term storage is designed for cost efficiency, offering lower costs for data accessed less frequently. Managing data at this scale requires balancing storage types to minimize expenses while ensuring data availability and performance.

One GiB equals 1,024 MiB or 2 to the power of 30 bytes. Similarly, one tebibyte (TiB) equals 1,024 GiB or 2 to the power of 40 bytes.

For instance, if 1 GiB of storage costs $0.02, then 1 TiB (approximately 1,000 GiB) costs $20.

It's important to note that storage costs vary by location. For example, active storage may cost $0.023 per GiB in Mumbai (Asia-south1) compared to $0.02 per GiB in the US (multi-regional) or EU (multi-region).

Suppose you want to import 10 TiB of HubSpot data into BigQuery. To calculate the cost for 10 TiB of active storage:

1. Convert 10 TiB to GiB: 10 TiB × 1,000 = 10,000 GiB

2. Calculate the cost at $0.02 per GiB: 10,000 GiB × $0.02 = $200

Therefore, importing and storing 10 TiB of data in active storage will cost $200. This straightforward calculation helps you estimate storage costs effectively and manage your BigQuery budget.

A default storage billing dataset for new datasets in BigQuery can now be set for your whole organization or project. 

BigQuery offers two storage billing options:

• Physical Storage Billing: Costs $0.04 per GB per month for active storage and $0.02 for long-term storage.

• Logical Storage Billing: Costs $0.02 per GB per month for active storage and $0.01 for long-term storage.

Physical storage billing is based on the actual amount of storage space consumed on the disk, including overhead for redundancy, metadata, and other internal storage requirements.

Logical storage billing is based on the logical size of the data, which is the size of the actual data as seen and managed by the user, not accounting for any internal storage overheads. 

Understanding the difference between physical and logical storage billing is crucial for optimizing costs and managing resources effectively in GCP. Physical storage billing provides a more comprehensive measure of total storage usage, while logical storage billing simplifies billing to reflect the user-visible size of the data. 

You can preset storage billing to physical at the organization or project level, simplifying the process for new datasets. For optimal cost efficiency, regularly monitor datasets and choose the appropriate storage billing option for each dataset individually.

Query pricing in BigQuery includes various models to suit different usage needs. Understanding these models helps optimize query performance and manage costs effectively.

In BigQuery, on-demand query pricing charges are based on the volume of data processed per query. This model offers flexibility for varying workloads, allowing users to pay only for what they use. It’s ideal for unpredictable query volumes, providing a scalable and cost-effective solution for data analysis without upfront commitments.

​Suppose you run a query that processes 500 GiB of data. With on-demand pricing, the cost would be calculated as follows: 

• Cost per GiB: $0.00625
• Total cost: 500 GiB x $0.00625 = $3.125

This model is ideal for users with variable workloads who want to avoid upfront commitments.

Flat-rate pricing, now deprecated, allowed users to pay a fixed monthly fee for a specified processing capacity. This model provided cost predictability but has been replaced by more flexible pricing options.

For example, a flat-rate pricing of $2,000 per month for a capacity that could handle up to 100 slots. However, with the new pricing structure, customers must choose between the Standard, Enterprise, and Enterprise Plus editions based on their specific needs and workloads.

BigQuery offers a capacity-based analysis pricing model for customers who prefer predictable costs over the on-demand price per terabyte of data processed. To enable capacity pricing, users can utilize BigQuery Reservations.

BigQuery editions provide different options for slot capacity:

• Standard Edition: Entry-level option with basic features.

• Enterprise Edition: Offers enhanced features for larger organizations.

• Enterprise Plus Edition: Includes the most advanced features for enterprises with significant data processing needs.

Key Features of BigQuery Editions:

• Slot Capacity: Consumed query processing capacity is measured in slots rather than billing for bytes processed.

• Coverage: Applies to query costs, including BigQuery ML, DML, and DDL statements. It does not cover storage costs, BI Engine costs, streaming inserts, or the use of the BigQuery Storage API.

• Autoscaling: The BigQuery autoscaler can be leveraged to automatically adjust slot usage based on demand.

• Billing: Slot usage is billed per second, with a one-minute minimum.

Optional Slot Commitments:

• Available for one or three-year periods within the Enterprise and Enterprise Plus editions.

• Regional capacity commitments are fixed to a specific region or multi-region and cannot be transferred.

• Commitments are shared across the entire organization, removing the need for separate commitments for each project.

• Offered with a 100-slot minimum and increments of 100 slots, ensuring flexibility and scalability.

• Automatically renewed unless set to cancel at the end of the period.

Standard Edition

In the Standard Edition of BigQuery, slot usage is billed based on a pay-as-you-go model, offering flexibility with no long-term commitment. The pricing can be adjusted based on regions. The cost structure is as follows:

Enterprise Edition

The Enterprise Edition offers more advanced features and the flexibility to choose between a pay-as-you-go model or committing to one-or three-year periods for cost savings. Like Standard Edition, Enterprise pricing can also be adjusted based on region. The pricing structure for this edition is as follows:

Enterprise Plus Edition

The following table outlines the pricing structure for the Enterprise Plus Edition of BigQuery, specifically for the US region. This pricing varies based on the commitment model chosen, offering flexibility for different business needs. Whether you opt for a pay-as-you-go model or commit to a longer-term agreement, the costs are billed on a per-slot hour basis. The cost differences are clearly detailed below, and region-specific pricing can be calculated.

By choosing the appropriate pricing model, your organizations can manage your BigQuery costs effectively while ensuring you have the necessary resources for your data processing needs.

Conducting a thorough cost analysis of BigQuery involves reviewing query expenditures and storage usage. Tools like the BigQuery cost calculator provide detailed insights, enabling users to estimate expenses accurately and identify areas for cost optimization, ensuring efficient budget management.

You can use the built-in cost analysis tools to monitor query costs in Google BigQuery using the console. These tools provide detailed insights into your query expenditures, helping you manage your budget and optimize your queries for cost efficiency. 

Below is an example of how you can use SQL to analyze the cost of queries in BigQuery:

SQL Code to Check Query Costs in BigQuery

You can query the INFORMATION_SCHEMA.JOBS_BY_* views to get detailed information about your queries, including their costs.

Explanation:

• project_id: The ID of the project where the query was run.

• user_email: The email of the user who ran the query.

• job_id: The unique identifier for the job.

• query: The SQL query text.

• total_bytes_processed: The total number of bytes processed by the query.

• total_bytes_billed: The total number of bytes billed for the query.

• total_gb_processed: The total data processed by the query in gigabytes.

• total_gb_billed: The total data billed by the query in gigabytes.

• creation_time: The timestamp when the query was created.

• end_time: The timestamp when the query ended.

• estimated_cost_usd: The estimated cost of the query in USD, assuming a cost of $0.00625 per GiB billed.

Steps to Run the Query:

1. Open the BigQuery console in the Google Cloud Console.

2. Navigate to your project and select the dataset where you want to save the query results.

3. Click on "Compose new query."

4. Copy and paste the above SQL code into the query editor.

5. Click "Run" to execute the query and view the results.

Running this query regularly can monitor your query costs, identify high-cost operations, and optimize your queries to ensure cost-efficient data processing.

The BigQuery cost calculator allows users to estimate costs based on projected usage scenarios. Users can accurately forecast potential expenses by inputting data volume, query frequency, and storage needs. This tool aids in planning and budgeting, ensuring informed decisions and efficient cost management by providing detailed cost breakdowns for different usage patterns.

BigQuery offers flexible on-demand pricing, charging users based on the quantum of data processed per query. This model is ideal for variable workloads, allowing users to scale query operations without upfront commitments and ensuring cost-effective data processing.​

Steps:​  

1. Open the GCP Pricing Calculator: Navigate to the Google Cloud Pricing Calculator.

2. Select BigQuery: Add BigQuery to your estimate.

3. Select Service type: Choose On-Demand.

4. Select Location type and Location: Add location where you will run queries and store data.

5. Enter Data Processed: Input the estimated amount of data queried and the amount of uncompressed data stored in BigQuery per month.

6. View Cost Estimate: The calculator will display the estimated monthly cost based on your input.

Reserved slots in capacity pricing provide predictable costs and scalability for high-volume query operations. This model suits organizations with consistent, high-volume querying needs, offering cost savings and performance guarantees, making it a strategic choice for large-scale data processing.​

Steps:
1. Open the GCP Pricing Calculator: Navigate to the Google Cloud Pricing Calculator.

2. Select BigQuery: Add BigQuery to your estimate.

3. Select Service type: Choose Editions.

4. Select Location type and Location: Add location where your BigQuery data will be stored and processed.

3. Choose Capacity Pricing: Select the option for capacity pricing.

4. Enter Slots Reserved: Input the number of slots you plan to reserve.

5. View Cost Estimate: The calculator will display the estimated monthly cost based on the reserved slots.

Accurately estimating storage and query costs involves analyzing data usage patterns and leveraging BigQuery's cost estimation tools. By understanding your data access and query patterns, you can make informed decisions on storage and processing needs. Here’s an illustration to give you a clearer picture:

Example Cost Calculations:

1. Storage Costs: Monthly storage costs are typically $0.02 per GB for active storage and $0.01 per GB for long-term storage. For instance, storing 1TB (1000GB) of active data would cost around $20 monthly.

2. Query Costs:

• Processing Cost: The cost to process 1TB of data is approximately $6.25.

• Small Query: Executing a 12 GiB query costs around $0.075.

• Medium Query: Executing a 100GB query costs about $0.625.

• Large Query: Running a query that processes 500GB of data would cost about $3.125.
  

Practical Application Example:

Scenario 1: You run ten daily queries, each processing 12 GiB. The daily cost would be 12 * $0.0625 = $0.075. Monthly, this totals approximately $2.25.

Scenario 2: You run a single, extensive query processing 1TB once a week. The weekly cost would be $6.25, resulting in a monthly cost of $25.

While views in BigQuery don't incur additional storage charges, querying them can result in processing costs based on the data processed. These expenses should be factored into data architecture design, as frequent queries on views can raise costs. Understanding these charges helps optimize query design and manage expenses effectively.

Depending on the size and source of the data, importing it into BigQuery may incur costs. Factors include data transfer, initial processing, and storage fees. Understanding these costs is essential for budgeting and planning data migrations, ensuring efficient and cost-effective data integration. Careful planning can minimize these expenses and streamline the data import process.

Data ingestion into BigQuery may incur costs based on the data size and source. Charges can arise from data extraction processes and transfer fees, mainly when importing large datasets. Understanding these expenses is crucial for effective budgeting and planning, ensuring that the integration of external data remains cost-efficient and streamlined within your data management strategy.

The BigQuery API can generate costs based on the number of API requests and the volume of data processed. Efficient API usage is crucial to managing these expenses. Monitoring and optimizing API calls helps control costs and maintains budgetary constraints, ensuring that the API operations are cost-effective and aligned with organizational needs.

The Storage Read API in BigQuery operates on an on-demand pricing model, which is entirely usage-based. All customers receive a complimentary tier of 300TB per month. However, it is important to note that data read from temporary tables does not count towards this free tier, and you will be charged on a per-data-read basis for such data.

Additionally, if a ReadRows function fails, you are still billed for all the data read during that session. If you cancel a ReadRows request before the stream concludes, you will be charged for any data read up to the point of cancellation.

Managing costs in Google BigQuery is essential for organizations seeking to leverage its powerful data analytics capabilities without overspending. Without careful planning and optimization, expenses can quickly escalate. The following are some tips that can help optimize BigQuery costs.

Optimizing query performance by clustering tables reduces the amount of data scanned, lowering query costs. This technique organizes data to enhance query performance, making data retrieval faster and more cost-effective, especially for large datasets. Effective clustering can significantly reduce query times and associated costs.

Setting custom quotas to limit query and storage usage can prevent unexpected expenses and ensure budget adherence. This proactive approach ensures that resource usage remains within budgetary constraints, providing better control over data processing costs and enhancing financial planning. Custom quotas help maintain cost predictability and resource allocation.

Optimizing data streaming by batching inserts and leveraging free-tier limits can reduce costs in BigQuery. Implementing efficient streaming strategies helps manage data ingestion expenses, ensuring continuous data flow without incurring significant costs. These strategies maintain budget efficiency while supporting real-time data updates and analysis, crucial for dynamic data environments.

The preview function in BigQuery allows users to examine data without incurring total query costs. This feature enables efficient data exploration and validation, helping users manage expenses by reducing the need for costly data scans. Utilizing previews for initial data checks and validations can lead to significant cost savings and improved data handling.

The GCP price calculator provides detailed cost estimates based on specific usage scenarios, aiding in accurate budgeting and financial planning. Users can forecast expenses accurately by inputting anticipated data volumes and query frequencies. This tool helps understand potential costs, make informed decisions, and manage resources for cost-efficient data management.

Here is a quick YouTube Video tutorial about using GCP Calculator

Decomposing large queries into smaller, manageable parts helps reduce the data processing load in BigQuery. This approach minimizes query costs by limiting the amount of data scanned, optimizing performance, and ensuring more efficient and cost-effective data analysis. Breaking down queries enhances query speed and reduces the overall cost of data operations.

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