BigQuery Pricing Mastery: How to Save Up To 70% of Costs

BigQuery Pricing Mastery: How to Save Up To 70% of Costs

BigQuery, Google's powerful data warehouse, empowers businesses to unlock valuable insights from massive datasets. But with great power comes, well, cost considerations. Understanding BigQuery pricing and implementing innovative cost-saving strategies ensures you maximize your data analysis investment.

Understanding Google BigQuery Pricing Model

Within the Google Cloud Platform (GCP), BigQuery charges for two main aspects of data management: storing your data (BigQuery storage) and analyzing it using queries (query data).

BigQuery Storage Pricing:

This refers to the space your data occupies within the Google Cloud storage. There are two pricing options for BigQuery storage:

  • Active Storage: This is the default storage for data you access frequently. You pay a flat-rate price of $ 0.02 per GB per month.

  • Long-term Storage: For rarely access data, you can move it to a cheaper tier at $ 0.01 per GB per month.

Query Costs (Query Data Processing):

This refers to the cost of running analyses on your data stored in BigQuery. BigQuery offers two pricing models for processing your query data:

  • On-demand pricing: This is a pay-as-you-go model based on how much data your queries process (data scanned). You're charged $5 per TB scanned. This is ideal for occasional queries or workloads with unpredictable usage.

  • Capacity-based pricing (BigQuery Editions): Ideal for predictable workloads with frequent queries where you can leverage flat rate pricing for potential cost savings. Here, you purchase a set amount of compute capacity (BigQuery slots) for a fixed monthly or yearly cost. You're charged within that allocated capacity, regardless of the specific amount of query data scanned within a slot. This can be significantly more cost-effective than on-demand pricing if your workloads are consistent.

Clusters are not directly related to Google BigQuery pricing, but understanding the concept is good. Clusters are groups of machines that work together to process queries. BigQuery automatically manages these clusters behind the scenes, so you don't need to worry about them for billing purposes. However, understanding that query complexity can affect how many resources (and potentially cost) a query uses is helpful.

Estimate the cost to share with your team using Google pricing calculator.

Optimizing Your BigQuery Costs

Keeping your BigQuery expenses under control is essential for maintaining efficiency while utilizing Google's powerful data tool.
Here are some strategies to ensure you stay within budget for both storage and analysis pricing:
  • Monitor costs closely: Utilize BigQuery's built-in cost-monitoring tools to track your spending and identify trends for optimization.

  • Optimize queries: Review your query structure to reduce the amount of data processed. Techniques like partitioning and materialized views can help improve efficiency. 

  • Utilize cost-effective storage options: Take advantage of BigQuery's storage tiers, including long-term storage for archived data. Choose the tier that best fits your needs to reduce costs. 

  • Schedule queries: Run your queries during off-peak hours to take advantage of lower rates and reduce expenses. Enable data deletion: Regularly delete unnecessary data to minimize storage costs and keep your dataset lean.

  • Employ a BigQuery cost audit service: Consider using professional services (such as Crystalloids) to analyse your usage to identify hidden cost-saving opportunities and optimize your spending based on the number of bytes processed.

Case Study: BigQuery Pricing Strategies for 77% Cost Reduction

Google BigQuery can become a significant cost burden if not utilized efficiently. This case study explores how we helped a client drastically reduce their BigQuery expenses through strategic improvements in both storage pricing and analysis pricing.

Our e-commerce client faced unexpected cost surges due to inefficiencies in their BigQuery usage. A series of processes implemented by another company resulted in significant cost increases, specifically involving updating and enriching data in BigQuery hourly. This approach led to:

  • Frequent Updates: Individual row updates were driving up costs. BigQuery excels at large-scale analysis, not frequent updates.

  • Excessive Data Scanning: Queries retrieving all fields from tables resulted in unnecessary data scanning.

  • Unpartitioned Tables: Scanning all data in large tables incurred high costs.

The Optimization Journey

To address these issues, we implemented a three-pronged approach that significantly reduced our client's BigQuery monthly cost:

  1. Updates and Batch Processing:
    We replaced individual updates with a caching mechanism and implemented batch updates at the end of each process. This dramatically reduced the number of updates and associated storage costs. Additionally, it minimized the number of times data needed to be refreshed in BigQuery, potentially reducing the frequency of running expensive SQL queries.

  2. Selective Field Usage:
    We meticulously analyzed the client's SQL queries to identify and retrieve only essential data types (columns) during analysis. This significantly reduced the amount of data scanned by each query, leading to substantial cost savings. By focusing on the most relevant data types, we ensured our client wasn't paying to analyze unnecessary information.

  3. Harnessing Partitioned Tables:
    We converted the client's tables to partitioned tables based on logical divisions like date or product category. This optimized querying by allowing BigQuery to only scan the relevant partition for a specific query, minimizing the data scanned and further reducing overall costs.

BigQuery Pricing Mastery: How to Save Up To 70% of Costs

Monthly comparison of cost reduction

The Result:

The optimizations that we implemented for our client yielded remarkable results. Within 20 days, they experienced a 38% decrease in their BigQuery expenses. After one month, a side-by-side comparison with the previous month revealed a staggering 77% cost saving.

By identifying and rectifying storage and analysis pricing inefficiencies, we implemented substantial cost-saving tactics and enhanced their current processes' performance. Overall, it was a resounding success story in optimizing BigQuery pricing.

Ready to optimize your BigQuery costs?

Implement these strategies or consider seeking professional help for a tailored approach. Crystalloids offers expert services to help you minimize both per-query and data storage costs, maximizing your return on data analysis investment. This ensures you leverage the full potential of BigQuery's cost-saving features.

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