Playgendary Cuts BigQuery Costs by 50%
DoiT's BigQuery Lens gave the mobile game studio granular cost visibility and query-level optimization across Google Cloud.
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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What they say
What I really like about DoiT's approach is that you're very hands-on and proactive. Satyam would ping me a few times a sprint, letting me know about the most current features, checking in on how things are going. When we are going through a peak time, that proactiveness makes a real difference. Satyam always comes through whenever we need support and helps us leverage the right experts to get us where we need to be.
Chiamaka Ibeme, Engineering Manager, Platform
SELECT has made important cost data readily accessible. I will often pull it up during engineering design reviews so we can quickly evaluate cost impact and projections and factor that into our design decisions.
Douglas Zickuhr, Senior Data Platform Engineer at Personio
I love clicking through SELECT to understand how our environment and workloads are evolving. I probably check it every day. It's coffee and SELECT for me every morning.
Ian Fahey, Senior Analytics Engineer at Loop
You guys have the best UI experience that I've had of any software. It's like you just read my mind where, like, oh, I wish I could click there. Oh, I can
Diana Koshy, Sr. Director of Data Engineering at Kargo
SELECT feels like exactly what Paul and I would have built if we had locked ourselves in a room for 18 months to create our ideal monitoring solution.
Devin McGee, Data Engineering Lead at Home Chef
SELECT dramatically lowers the cognitive load to understanding Snowflake costs. I'm able to sit there and easily understand what's driving the cost. Not to blow smoke up your ass, but it's just so easy to do in your platform
Blake Baggett, Head of Data Operations at Entain
One of the most helpful cost rituals we've setup from SELECT is the weekly spend digest sent to Slack. I can start high level and ensure things are in check. If not, I can very quickly drill down into specific workloads which may have driven the cost spike and remediate them before they become a bigger issue.
Michael Revelo, Manager of data and analytics engineering at ClickUp
Through SELECT's automated savings feature and deep cost visibility, we were able to instantly lower our Snowflake spend by over 40% and achieve a 20X ROI on our SELECT investment.
Skyler Chi, SVP, GTM Productivity & Excellence at Exiger
Our costs had jumped up 3X as we scaled, so we're talking about 60% savings in Snowflake spend after adopting SELECT.
Edward Mancey, GTM Lead at Synthesia
DoiT was a true partner, not a vendor. They helped us understand the problem, refine the vision, and build something production-ready far faster than we could have on our own. Their expertise, responsiveness, and commitment made all the difference.
Dr. Anish Kapur, Founder & CEO, Promptly
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