BigQuery Cost Optimization: Simple Strategies to Save Money

Have you ever opened the billing section of a BigQuery account and got a shocking surprise? You are not alone. BigQuery is a powerful tool, but this power does not come for free all the time. It can quickly deplete your budget if you do not practice good cost management. BigQuery is one of the most popular cloud data warehouses, allowing organizations to handle massive datasets efficiently. You can process enormous amounts of data in seconds, but it also comes with cost management. A company’s expenses may increase by 60% in just three months due to its rapidly expanding data and scaling needs in BigQuery. They did not optimize their storage or queries, which might have caused increased costs.

We will explore some practical strategies for BigQuery cost optimization. Whether you’re starting with BigQuery or have been using it for a while, these cost-saving strategies and best practices will help you maximize efficiency.

What is BigQuery?

BigQuery is a powerful data warehouse that allows you to run SQL queries on massive datasets. It is a great solution for businesses that need to process large amounts of data for analytics and processing. You only need to worry about the data, not the infrastructure.

BigQuery’s architecture divides storage and computation resources. You need to pay separately for data storage and query execution. This allows users to scale their consumption based on their requirements, this is where bigquery cost optimization comes in, making BigQuery a versatile and cost-effective solution. Tables are utilized for storing data, and they can be partitioned and clustered to reduce the amount of data processed and enhance query performance. This is where the main bigquery cost optimization techniques come into play; you can fine-tune your setup to save expenses by knowing how BigQuery handles queries and storage.

BigQuery Pricing and Storage Model

BigQuery pricing can be divided into two main categories: storage and query costs. Google offers two pricing models for query costs:

1. On-Demand Pricing
   You pay $5 per terabyte of data processed by your queries. You pay as you query more data. This is ideal for users who run queries on an infrequent basis.

2. Flat-Rate Pricing
   You pay a fixed price, starting at $2000 for 100 slots (a slot measures reserve query processing capacity) per month. Flat-rate pricing is great for businesses with consistent work and query loads as it enables better cost control.

The initial 10GB of data storage per month is free for storage costs. When the 10 GB allowance finishes, users pay $0.02 per gigabyte for active storage (data that has been modified within 90 days) and $0.01 per GB for long-term storage (data that has not been modified for 90 days). This model is cheaper to store historical data without affecting access speed.

BigQuery Cost Optimization – Best Practices

BigQuery Cost optimization includes managing storage, improving query efficiency, and selecting the right pricing model. A few key changes can minimize your BigQuery costs while maintaining performance and scalability.

1. Use Partitioned Tables for Time-Based Data

One of the easiest ways to reduce query costs is to use partitioning. When tables are partitioned, BigQuery only processes the data chunks relevant to the query. 

For example, we have a dataset of user activity logs. We can partition the table by date as log data can be massive. This will ensure that only the necessary date ranges are being scanned, not the entire table, significantly reducing the amount of data processed.

CREATE OR REPLACE TABLE `project.dataset.user_activity`
(
  user_id STRING,
  action STRING,
  activity_time TIMESTAMP,
  device STRING
)
PARTITION BY DATE(activity_time)

There are two ways to implement this:

1. We can use TIMESTAMP or DATE columns to partition your tables. 
2. We can also use WHERE clauses, which limit the data scanned by applying time/date conditions to the data. 

2. Clustering for Faster Queries

Data clustering organizes data within each partition based on columns. It allows BigQuery to quickly find its needs without scanning the whole data. Clustering automatically sorts the data in each partition according to your chosen columns. For example, using the activity logs data, the column could be user ID, connection ID, or region. This increases efficiency as BigQuery can skip unnecessary rows. The result is faster query performance and lower costs since less data is scanned.

CREATE TABLE clustered_activity_logs
PARTITION BY DATE(activity_date)
CLUSTER BY user_id AS
SELECT * FROM activity_logs;

There are two ways to implement this:

1. We can choose the columns for clustering that align with your most queried data.
2. We can use both clustering and partitioning to maximize efficiency.

3. Avoid SELECT

Using ‘SELECT’ is one of users’ most common blunders when querying data. Users quickly query data by adding ‘SELECT   from table’ to view or test out queries. However, they are unaware that using `SELECT` is one of the most common causes of high query costs. As it is apparent, it retrieves all the columns in a table, even if your analysis needs two or three. For example, activity_logs data usually have hundreds of irrelevant and insignificant columns that are not crucial for analysis. Using this SELECT, all of those columns will be retrieved. BigQuery charges based on the amount of data processed. Hence, you can reduce the data size scanned and thus lower your costs by choosing specific columns.

There are two ways to implement this:

1. Specify the columns you need in your queries.
2. Use the query optimization and analyzer tools to identify redundant columns.

4. Query Scheduling and Caching

There is a section called Scheduled Queries in BigQuery. If your queries need to run on a schedule, leverage BigQuery’s query scheduling feature to ensure the same query is not running repeatedly. Scheduled queries automate repetitive jobs while ensuring they are running efficiently. BigQuery caching feature automatically stores query results temporarily. BigQuery retrieves the same results from the cache at zero cost if the query is repeated within 24 hours and data remains the same.

There are two ways to implement this:

1. Use the scheduled queries feature. Create a schedule that meets your needs. 
2. Take use of cached results by ensuring that searches are structured consistently across datasets.

5. Leverage Flat-Rate Pricing for Known Workloads

The flat-rate pricing model might be a good option for you if you have a consistent and heavy workload. The on-demand pricing model charges per TB of data processed, but flat-rate pricing gives you a set of processing slots for a fixed price, starting at $2000 for 100 slots. This option will save you a lot of costs.

There are two ways to implement this:

1. Check your workloads to see if your usage pattern is consistent enough to benefit from flat-rate pricing.
2. Utilize the BigQuery cost calculator to estimate the costs based on your usage. You can then forecast your potential savings.

6. Data Deduplication and Compression

Redundant data is unnecessary and inefficient. It only increases both the storage and query costs. Hence, it is necessary to use data deduplication techniques to reduce the amount of storage used. There are also other ways, such as using other data compression formats, such as Avro and Parquet, to compress your data further. It can minimize storage costs without compromising access speed.

There are two ways to implement this:

1. Data transformation techniques are used in the ETL process to regularly clean and remove duplicate records.
2. Consider using other compressed data formats to reduce the amount of data scanned.

7. Use the BigQuery Data Transfer Service  

Google provides a service known as the BigQuery Data Transfer Service. It automates data import from external sources like Google Analytics, Google Ads, YouTube, and SaaS apps. The service easily connects with these external applications and saves time ingesting data. Manually loading data is inefficient, of course! Automating these processes ensures the data is transferred efficiently, reducing the probability of loading excessive or redundant data.

There are two ways to implement this:

1. Set up automatic transfers for the external data sources you use frequently.
2. Monitor and manage these transfers to avoid unnecessary data loads.

8. Set Billing Alerts

Cloud Billing Alerts help you regulate your costs and alert you when they exceed a certain threshold. You can set an alert to notify you when the current month’s costs exceed the previous month’s costs. It is essential to have visibility on your BigQuery usage to avoid unexpected costs. As a result, you can quickly optimize your queries if needed.

There are two ways to implement this:

1. Setting up Cloud Billing Budgets and Alerts in Google Cloud Console.
2. Monitor your usage with BigQuery Cost Control reports regularly.

Are there any Free BigQuery Operations?

Yes, there are a couple of free operations in BigQuery that you can utilize for your benefit:

1. 10 GB Free Storage
   As discussed, BigQuery gives all users a free allowance of 10 GB of data each month. This allows any user to learn and explore BigQuery without any cost stress. You can test out queries by uploading data and learning the features. Furthermore, data is moved to long-term storage after 90 days, billed at $0.01 per GB compared to $0.02 for active storage.

2. Free Query Execution
   BigQuery provides 1TB of free data processing per month! This is a perfect deal for startups with small-scale businesses. You can also experiment if you are working on small-scale datasets and playing with the platform. You can also perform your analytics within the free tier.

3. Free Manual Data Loading and Exporting
   BigQuery does not charge for manual data loading or exporting using CSV files or other formats. Hence, you can move data into or out of BigQuery at no cost. This is super great if you want to archive or migrate data to BigQuery for quick analytics. 

4. Free Metadata Queries
   You can retrieve metadata using queries free of cost, such as information about schemas or checking the size of datasets. 

Why BigQuery Cost Optimization?

There are four important points that we need to emphasize for bigquery cost optimization:

• Scalability
  Data volumes will grow, and if you do not optimize costs, costs will grow as well. Applying BigQuery cost optimization will ensure that you can manage increased data without costs going out of control.
• Predictability
  BigQuery provides cost forecasting and estimation tools that help you create budgets and allocate necessary resources. This is very important for small businesses with tight budgets or limited resources.
• Resource Efficiency
  BigQuery allows you to pay as you use it. You ensure that you only pay for the resources that you actually use. You avoid over-allocating resources that may unnecessarily increase costs.
• Maximize Return On Investment
  BigQuery Cost optimization increases return on investment in data analytics. You should maximize the value you can take from BigQuery when using its capabilities while controlling costs. Optimizing costs ensures a better return on investment (ROI) in data analytics.

Conclusion

BigQuery is a powerful and flexible data warehouse, but it requires careful management to control costs and avoid unnecessary expenses. 
By leveraging strategies and best practices as discussed in this blog, which include but are not limited to partitioning, clustering, and query optimization, you can significantly reduce query and storage expenses. We should take advantage of free operations that BigQuery provides to our best and use the billing and budgeting tools to stay within the budget while maintaining performance. BigQuery Cost optimization maximizes return on investment and enables you to scale your data analytics solutions in BigQuery. Now, you are on track to leverage BigQuery fully.

Frequently Asked Questions on BigQuery Cost Optimization