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Overview and Use Cases

Data Warehouse provides direct access to your data via Google BigQuery. This enables custom querying, integration with external tools and advanced analysis beyond what’s available in AnalyticsIQ.

Using Data Warehouse removes the need for setting up your own SDK, processing pipeline, storage and data warehouse solution.

Use Data Warehouse to:

  • Conduct complex joins and transformations across datasets
  • Integrate with BI tools, machine learning models or internal toolsets
  • Enrich or correlate data with external sources
  • Build custom reporting and KPIs

Use Case Examples​

  • Machine learning data pipelines: Train models for churn prediction, pLTV, player segmentation, or dynamic pricing
  • Cross-title player tracking: Identify users who play multiple games across your organization using shared identifiers
  • Ad and IAP behavior analysis: Explore correlations between specific gameplay events and monetization actions, such as ad views or in-app purchases
  • Device-level ROI analysis: Combine revenue data with estimated CPI figures from attribution sources to assess ROI
  • Cross-source revenue validation: Compare in-game revenue data with external sources (e.g. attribution platforms) to validate accuracy and consistency

Data Access​

Once you order Data Warehouse, we’ll generate your organisation's dedicated Data Warehouse using a Google Cloud Platform (GCP) project.

To provide you access to Data Warehouse, we request a google group instead of a specific email. This method allows you to manage who has access to Player Warehouse by controlling who is in the group. If you need help creating a google group, check the steps in Google's documentation here.

The data for your chosen games will be available in Google’s Cloud Data Warehouse, BigQuery; feel free to check its official documentation here or go to the next page for a brief introduction to BigQuery and Data Warehouse.

Billing​

Data Warehouse has a billing view, allowing you to track your query spending.

This view is a subset of the BigQuery billing table, and you can find all the fields and definitions in their documentation.

Export to S3​

If you prefer to receive the data in AWS S3 or Google Cloud Storage, we can also export the files (with the same schema) in Parquet format. Read about our Data Export feature here