Some time ago, a few friends and I decided to build an app. We duck-taped our code together, launched our first version, then attracted a few users with a small marketing budget.
Some time ago, a few friends and I decided to build an app. We duck-taped our code together, launched our first version, then attracted a few users with a small marketing budget.
The value of good analytics at this stage of a product’s lifecycle can not be overstated. Your product metrics are like the vital signs of your app. To monitor its health and growth, we decided to use Firebase Analytics.
In true start-up fashion, we rapidly looped through the development cycle. Designing small experiments at each step saw our product vitals steadily improve.
We had approached the point of diminishing returns when it came to using the analytics dashboards to inform our enhancements. Our experiments needed access to more data. We hit a wall — Firebase’s paywall that guards access to all the data you could want about your product.
Why not just pay for the upgrade? Headlines like the one below were enough to make us think twice
And so, we had a new challenge:
“How do we get a clean dataset from the analytics dashboards?”
In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.
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