Datasets & Schemas Overview
Event Types
As a GameAnalytics user, you are likely already familiar with the types of events used to track different concepts in the game. You can find detailed information on this topic at Event Types.
| Event Type | Description |
|---|---|
| Business | In-App Purchases supporting receipt validation on GameAnalytics servers. |
| Progression | Level attempts with Start, Fail & Complete event. |
| Resource | Managing the flow of virtual currencies – like gems or lives. |
| Error | Submit exception stack traces or custom error messages. |
| Design | Submit custom event id’s. Useful for tracking metrics specifically needed for your game. |
| Ads | Ad actions such as ad clicks and ad impressions. |
| Impression | Track impression data shown from ads shown using different ad partners. |
Most of these types are represented in the Data Warehouse in different tables. In addition, we make tables with varying aggregation levels available to you, so you can choose which one to query depending on the use case.
In Data Warehouse you can find three datasets: checkpoint, events, and device_info.
Checkpoints dataset
The checkpoint tables are aggregated daily. These tables have up to 1 year of data and will be available as soon as you sign up, as long as your game has been sending events to GameAnalytics.
The player_checkpoint table has the bulk of the information about the player, and can be used to join with all the other tables on the user_id, game_id or player_id.
The tables are:
| Table | Filter | Aggregation | Description | Use Cases |
|---|---|---|---|---|
| Player | All players | Player id | Includes information and statistics per player, anything from errors, event or session counts, if it’s a paying user, etc. | Track the players assigned to a specific A/B Testing experiment ,or figure out if there is any specific device type that is causing most critical errors. |
| Payment | Business Events | Transaction | Includes information on all In-App Purchases / business transactions. | Understand which item is the most profitable one in your game, or which currency is bringing in the most revenue. |
| Progression | Progression events | Event id | Includes statistics on levelling events (such as level Start, Fail or Complete and scores). | Segment your pool of players depending on how good they are at the game (scores), or how far ahead in the game they already are (levels). |
| Resource | Resource events | Event id | Includes virtual transactions, such as loosing lives or gaining gems. | Figure out which soft currencies are the most popular and easily earned by players. |
| Session | All sessions. | Session id | Includes information on all sessions, such as start and duration. | Dive into how players’ sessions vary over time. Do players play more or less during the weekends or during the holiday period? |
| Ads | Ad Events | Event id | Includes information on all ads, such as ad placement, clicks and impressions. | Based on the ad placement and ad action, gives us an insight of the ads shown and clicked by a player. |
| Impression | Impression Events | Event id | Includes information on all impressions, such as impression level revenue and counts. | Impression data could vary from each network. How much revenue is attributed to the impression events and the number of such events seen. |
Please note these tables have 1 year retention. If you wish to keep the data for longer you'll need to copy it to a different table or export it.
Reach out to us if you need assistance.
Events dataset
The event tables not aggregated, meaning each event will be represented by one row, giving you access to the timestamp of each event.
Because of this granularity, these tables have up to 30 days of data, and will only be populated once you start using Player Warehouse.
The tables are:
| Table | Filter | Description |
|---|---|---|
| adactivity | Ad events | Includes information of how players interact with ads and ads performance. |
| design | Design Events | Includes information on custom concepts of your game. |
| error | Error events | Includes information on error events, their severity and debug messages. |
| impression | Impression events | Includes information on impressions. |
| payment | Payment events | Includes information on all In-App Purchases / business transactions. |
| progression | Progression Events | Includes information on leveling events (such as level Start, Fail or Complete and scores). |
| resource | Resource Events | Includes virtual transactions, such as losing lives or gaining gems. |
Please note these tables have 30 days retention. If you wish to keep the data for longer you'll need to copy it to a different table or export it.
Reach out to us if you need assistance.
Device Info dataset
The device_info dataset contains enriched device hardware specifications and market information. This data is derived from device model identifiers and includes details such as release dates, hardware capabilities, and estimated market values.
The table is:
| Table | Description | Use Cases |
|---|---|---|
| device_info | Includes detailed device specifications such as CPU, GPU, RAM, storage capacity, display size and resolution, brand, release date, and estimated market value (USD). | Analyze performance across different device tiers, segment users by device capabilities, or understand which hardware is most popular among players. |
This dataset can be joined with other tables using the device model field to enrich player and event data with hardware specifications.
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.
Export to S3
If you prefer to receive the data in AWS S3 of Google Cloud storage, you can also receive the schema files in Parquet format using Data Export.