Introduction to BigQuery editions
BigQuery offers three editions (Standard, Enterprise, and Enterprise Plus) to support different workloads. These editions are an alternative to existing reservation models. You can use editions and on-demand models at the same time.
Each edition offers a set of capabilities at a different price point to meet the needs of different types of customers. You can create a reservation or a capacity commitment for an edition. Capacity commitments are not required to purchase slots, but can save you money. For more information about slots autoscaling, see Introduction to slots autoscaling.
BigQuery editions are a property of compute power, not storage, so you can query datasets regardless of how they are stored, as long as you are not using a non-matching edition-specific capability.
Editions features
BigQuery offers three editions: Standard, Enterprise, and Enterprise Plus. The Standard edition is the most basic, while the Enterprise Plus edition is the most advanced. Each edition has different pricing, compute model, max reservation size, max reservations per admin project, commitment plans, monthly SLO, compliance controls, data governance, storage encryption, BI acceleration, materialized views, search, unstructured data, integrated machine learning, workload management, supported assignment types, and multi-cloud analytics.
Overview of BigQuery pricing
BigQuery pricing is based on Compute pricing and Storage pricing . Compute costs are based on the amount of data processed, while storage costs are based on the amount of data stored. Other operations, such as using BigQuery Omni, BigQuery ML, BI Engine, and streaming reads and writes, are also charged.
Free operations
The following BigQuery operations are free of charge in every location. Quotas and limits apply to these operations.
Free usage tier
BigQuery offers free usage limits for some resources during and after the free trial period. You can try BigQuery’s free tier in the BigQuery sandbox without a credit card. If you go over the usage limits, you will be charged according to the pricing.
More BigQuery printing detail : Source
Google Cloud Pricing Calculator
GCP pricing calculator is a web-based tool that provides an estimate of the cost of using GCP services. It is a simple and efficient tool that can be used to understand how pricing works within Google Cloud. The pricing calculator takes into account the type and amount of resources used, as well as the region in which the resources are located. The estimated cost can be used to budget for GCP usage and to compare the costs of different GCP services.
The pricing calculator is a valuable tool for anyone who is considering using GCP services. It can help to ensure that you are aware of the costs associated with using GCP and that you are making the most cost-effective choices for your needs.
For information about BigQuery editions pricing, see BigQuery pricing.
Quotas
Slots from all editions are subject to the same quota. Your quota is not fulfilled on a per-edition basis. For information about quotas, see Quotas and limits.
For more information on slots autoscaling, see Introduction to slots autoscaling.
For more information on reservations, see Introduction to Reservations.
Save Money on BigQuery Storage with New Billing Model
This blog post discusses how to reduce BigQuery storage costs by using Physical Bytes Storage Billing (PBSB) instead of Logical Bytes Storage Billing (LBSB).
PBSB is a way to bill for storage, it takes into account the actual amount of data that you store. LBSB is another way to bill for storage, as it does not require you to track the actual amount of data that you store. The PBSB will include improved observability over table storage metadata. Typically, a 50%-90% compression ratio is observed for Physical Bytes.
Logical bytes and physical bytes
BigQuery offers two storage billing options: logical bytes and physical bytes. Physical bytes storage billing is more cost-effective for customers who use compression. To reduce storage costs, you can implement a two-step approach:
Migrate data to a dataset with physical bytes storage billing.
Compress data using BigQuery’s built-in compression capabilities.
First Approach: Conduct a BigQuery cost benefit assessment between PBSB and LBSB Google has provided an example of how to calculate the price difference at the dataset level using this query.
Running the query in the example above results in the following details in the table below;
The first datasets have a compression ratio of 16 to 25, reducing storage costs by 8x to $8,558/month. The last dataset uses 96% of active physical storage for time travel, unsuitable for PBSB. The “_session” and “_scripts” objects are billed based on logical bytes and can be disregarded by modifying the query with this clause
Second approach: To optimize storage costs, you can switch to physical storage billing for suitable datasets. However, you will not be able to do this if your organization has any flat-rate commitments.
To update the billing model for a dataset, use the BigQuery (BQ) update command and set the storage_billing_model flag to PHYSICAL. For example:
It takes 24 hours for the change to take effect.
Another factor to consider is time travel. Time travel allows you to query updated or deleted data, restore deleted tables, or restore expired tables. The default time travel window covers the past seven days, but you can configure it using the BQ command-line tool to balance storage costs with your data retention needs. For example:
This command sets the time travel window to 48 hours (2 days) for the specified dataset.
The –max_time_travel_hours value must be an integer expressed in multiples of 24 (48, 72, 96, 120, 144, 168) between 48 (2 days) and 168 (7 days).
Considerations
BigQuery storage pricing is based on the amount of physical storage used. If you use physical storage, you will be charged twice as much as if you used logical storage.
To benefit from physical storage billing, your compression ratio must be greater than 2.
In logical storage billing, you are not charged for bytes used for time travel storage. However, in physical storage billing, you are charged for bytes used for time travel storage. This is because physical storage is used to store both current and historical data.
To ensure accurate billing, it is advisable to conduct an evaluation of time travel storage utilization once your BigQuery workload has reached a stable state and established a predictable pattern. This is important because the bytes utilized for time travel storage can vary over time.
You can configure the time travel window according to your specific data retention requirements while considering storage costs. For instance, you can customize the time travel window, adjusting it from the default 7 days to a shorter duration such as 2 days.
A fail-safe period of 7 days will be enforced, during which the time travel setting cannot be modified. However, once this period ends, you can configure additional time travel days according to your preference. This configurable range extends from 2 to 7 days, allowing you to establish an effective time travel window spanning between 9 and 14 days. If no action is taken, the default time travel window will remain set at 14 days.
You can switch to physical bytes storage billing at any time. However, you must wait 14 days before you can change the billing model again.
Conclusion
Switching to BigQuery Physical Bytes Storage Billing (PBSB) can save money on BigQuery storage. The process of assessing and transitioning to this cost-effective billing model is straightforward, so you can maximize the benefits.
Please refer to more information :
Reducing BigQuery physical storage cost with new billing model
BigQuery Storage Billing Models
