Digital native companies have no shortage of data, which is often spread across different platforms and Software-as-a-service (SaaS) tools. As an increasing amount of data about the business is collected, democratizing access to this information becomes all the more important. While many tools offer in-application statistics and visualizations, centralizing data sources for cross-platform analytics allows everyone at the organization to get an accurate picture of the entire business. With Firebase, BigQuery and Looker, digital platforms can easily integrate disparate data sources and infuse data into operational workflows – leading to better product development and increased customer happiness.
How it works
In this architecture, BigQuery becomes the single source of truth for analytics, receiving data from various sources on a regular basis. Here, we can take use of the broad Google ecosystem to directly import data from Firebase Crashlytics, Google Analytics, Cloud Firestore and query data within Google Sheets. Additionally, third party datasets can be easily pushed into BigQuery with data integration tools like FiveTran.
Within Looker, data analysts can leverage pre-built dashboards and data models, or LookML, through source-specific Looker Blocks. By combining these accelerators with custom, first party LookML models, analysts can join across the data sources for more meaningful analytics. Using Looker Actions, data consumers can leverage insights to automate workflows and improve overall application health.