Meet Playgendary
As a mobile game developer with over 3 billion installs and 250 million monthly players, Playgendary knows a thing or two about user acquisition. A critical component of their user acquisition strategy is Google BigQuery, which they use to evaluate the effectiveness of their marketing campaigns.
The Challenge
Playgendary couldn't easily understand how their BigQuery costs were broken down at a granular level. Managing a team of data engineers and analysts, analyzing job-level data with SQL was too time-consuming. They also faced fluctuation in compute usage due to unpredictable variables like user activity and game popularity, making it difficult to purchase Committed Use Discounts.
The Solution
Playgendary leveraged DoiT's BigQuery Lens to understand cost breakdowns and identify optimization opportunities. They used personalized recommendations to remove unused tables and the Explorer feature to identify expensive queries without writing SQL. They also worked with DoiT's cloud architects to optimize storage costs and implement on-demand compute workloads commitment for Compute Engine savings.
Results
- Reduced BigQuery costs by over 50% in just one month
- Gained greater visibility into team's BigQuery usage and behavior patterns
- Saved 25% on Compute Engine costs without sacrificing on-demand flexibility
BigQuery is a critical component of our cloud infrastructure, but understanding how we could use it more optimally was difficult. Without BigQuery Lens, I wouldn't have been able to achieve any significant results around cost optimization. The easy-to-use drill down into my team's BigQuery usage and personalized recommendations made optimizing how we use it really easy.
Mikhail Artyugin, Director of Business Intelligence, Playgendary
Understanding and Optimizing BigQuery Costs
Mikhail used BigQuery Lens to understand how costs were broken down and identify optimization priorities. He leveraged recommendations to identify large unused tables and removed them. He used the Explorer feature to identify and optimize the most expensive queries per table and user without writing queries himself. After identifying queries, he examined execution flows in the BigQuery console to find issues like bad JOINs or missing predicate filters.
Optimizing Compute Engine Savings
To optimize Compute Engine spend, Playgendary worked with DoiT on their on-demand compute workloads commitment. They reduced overall Compute Engine spend by 25% without sacrificing on-demand pricing flexibility and without operational management effort. Most importantly, they realize these savings without needing to predict future success of new games.
Storage Optimization Strategy
Mikhail worked with DoiT Senior Cloud Architect Rajan Bhave to understand the pros and cons of using BigQuery's new Physical Storage before deciding they'd save money by switching. This collaboration helped them make informed decisions about storage optimization that contributed to their overall cost reduction goals.
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