Google Big Query Editions: Understand Your Options
BigQuery is a fully managed, serverless data warehouse provided by Google Cloud, designed to enable fast SQL queries and interactive analysis of massive data sets. It’s scalable and can handle petabytes of data, integrating with various data analysis and business intelligence tools.
By leveraging Google’s infrastructure, BigQuery provides users with a highly reliable platform for data analysis without the need for managing hardware or software. This lets organizations focus on extracting valuable insights from their data, rather than worrying about the underlying technology.
This is part of a series of articles about Google Cloud cost.
BigQuery Product Editions
Google offers three BigQuery editions, Standard, Enterprise and Enterprise Plus, as well as on-demand pricing.
Standard
The Standard edition of BigQuery offers a foundational level of service for data processing and analysis. It operates on an autoscaling compute model, which automatically adjusts compute resources based on the workload, ensuring that queries are processed efficiently without the need for manual scaling.
With a maximum reservation size of 1,600 slots and up to 10 maximum reservations per administration project, this edition is designed to cater to small to medium-sized workloads. Pricing for the Standard edition follows a slot-hour model with a one-minute minimum charge. However, it does not offer access to capacity commitments, which could reduce costs for more predictable workloads.
This edition comes with a monthly service level objective (SLO) of ≥99.9%, guaranteeing high availability for critical data processing tasks. It offers some degree of data security and compliance, such as Google-managed keys for storage encryption
The Standard tier does not provide more advanced Google Cloud features like:
- Compliance controls through Assured Workloads.
- Virtual Private Cloud (VPC) Service Controls.
- Fine-grained security controls including column-level access control, row-level security and dynamic data masking.
- BigQuery ML.
- BigQuery Omni.
- Advanced workload management features.
Enterprise
The Enterprise edition of BigQuery enhances the capabilities offered in the Standard edition. It maintains the autoscaling compute model but adds a baseline level of compute capacity, ensuring that resources are readily available for high-volume workloads.
This edition supports a larger number of maximum reservations (up to 200 per administration project) and offers capacity commitments with one-year and three-year plans, with respective discounts of 20% and 40%, for longer-term commitments.
The Enterprise edition offers a higher monthly SLO of ≥99.99%. It introduces compliance controls through Assured Workloads and VPC Service Controls, enhancing data security, regulatory compliance and security features such as column-level access control, row-level security, dynamic data masking and the option for customer-managed keys (CMEK) for storage encryption.
Additionally, this edition offers BigQuery ML for integrated machine learning capabilities, and BigQuery Omni, which enables running analytics queries on data stored in Amazon Simple Storage Service (Amazon S3) or Microsoft‘s Azure Blob Storage using BigLake tables.
Enterprise Plus
BigQuery’s Enterprise Plus edition is the most advanced offering, and extends the features of the Enterprise edition with more data security, compliance and analysis capabilities.
Like the Enterprise edition, it operates on an autoscaling compute model with a baseline for compute capacity, and supports up to 200 maximum reservations per administration project. Capacity commitments are available with the same discounts as the Enterprise edition for those who seek predictable billing and resource availability.
The Enterprise Plus edition provides the highest level of service availability with a monthly SLO of ≥99.99%. It offers the most comprehensive security and compliance controls, including custom data masking in addition to the security features available in the Enterprise edition.
This edition uniquely supports the creation, automatic refresh and direct query of materialized views with smart tuning, enabling more efficient query performance and faster insights. Like the enterprise edition, it provides access to BigQuery ML and BigQuery Omni.
On-Demand Pricing
The BigQuery on-demand pricing model is designed for flexibility, allowing users to pay for the compute resources they use, based on the amount of data processed, without any up-front costs or long-term commitments.
Under the on-demand model, charges are calculated based on the number of terabytes (TB) of data processed by queries. This approach ensures that users only pay for what they use, making it an economical choice for ad hoc analysis and projects with unpredictable data processing needs.
BigQuery provides a generous free tier under this model, allowing users to process the first 1TB of data each month at no charge, which is an attractive feature for smaller projects or for those in the exploratory phase of their data analysis.
Beyond the free tier, the cost per TB processed is straightforward and transparent, enabling easy budgeting and financial planning. The on-demand model does not require users to manage or reserve slots (virtual CPUs). The infrastructure scales automatically to meet the demands of the workload, without the need for manual intervention.
However, it’s important for users to monitor their queries and usage, as the cost can vary based on the complexity and size of the data sets being queried. BigQuery’s query validator tool can estimate costs before running queries, helping to avoid unexpected charges.
Best Practices To Optimize BigQuery Costs
When using BigQuery, it may be helpful to consider the following ways to optimize costs.
Preview Queries Before Running Them
Before executing queries in BigQuery, use the query validator feature to estimate their cost. This can prevent unexpected charges by providing an up-front cost prediction based on the data processed. It helps in planning and budgeting, especially for complex queries that could involve large data sets.
Avoid Using Queries To View Table Data
To minimize unnecessary compute costs, avoid using SQL queries for exploring or previewing table data. Instead, utilize BigQuery’s table previews and metadata details, which provide quick insights without incurring compute charges. This approach is beneficial for initial data exploration and understanding table structures.
For deeper analysis that requires querying, consider structuring queries to minimize the amount of data processed, thus optimizing costs. This involves selective use of columns, proper filtering and limiting query results.
Cap the Maximum Bytes Billed
BigQuery allows you to set a limit on the maximum number of bytes billed on queries. This can cap costs by preventing the execution of unexpectedly large queries. This safeguard ensures that if a query exceeds the specified data processing limit, it is not executed, protecting against unintended high charges.
Materialize Query Results in Stages
Materializing the intermediate results of complex queries into temporary or permanent tables can optimize subsequent queries and reduce overall processing costs. By breaking down queries into smaller, manageable pieces and storing the results, subsequent queries can be more efficient, processing less data.
Leverage the Google Cloud Pricing Calculator
The Google Cloud Pricing Calculator is a useful tool for estimating BigQuery costs, providing detailed forecasts for both storage and compute resources based on various inputs. Input the data volume, query frequency and other parameters to receive an accurate estimation of your potential costs.
What Does it Cost To Use BigQuery?
Now that you know about the different BigQuery pricing models and how to optimize their use, you’re probably wondering how much it actually costs. I’ll provide in-depth information about BigQuery costs for compute and storage, as well as additional features, in part two of this series.
For more insight, read our guide to GCP compute pricing.
