But why choose between one or the other pattern? A key benefit of this architecture is that you can write your data pipeline processing once and execute it in either batch or streaming mode without modifying your codebase. So if you start processing your logs in batch mode, you can easily move to real-time processing in the future. This is an advantage of the high-level
Cloud Dataflow model that was
released as open source by Google.
Cloud Dataflow loads the processed data into one or more BigQuery tables. BigQuery is built for very large scale, and allows you to run aggregation queries against petabyte-scale datasets with fast response times. This is great for interactive analysis and data exploration, like the example screenshot above, where a simple BigQuery SQL query dynamically creates a Daily Active Users (DAU) graph using
Google Cloud Datalab.
And what about player engagement and in-game dynamics? The BigQuery example above shows a bar chart of the ten toughest game bosses. It looks like boss10 killed players more than 75% of the time, much more than the next toughest. Perhaps it would make sense to lower the strength of this boss? Or maybe give the player some more powerful weapons? The choice is yours, but with this
reference architecture you'll see the results of your changes straight away. Review the new reference architecture to jumpstart your data-driven quest to engage your players and make your games more successful,
contact us, or
sign up for a free trial of Google Cloud Platform to get started.
Further Reading and Additional Resources- Posted by Oyvind Roti, Solutions Architect 
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