--- title: "AppsFlyer" slug: "appsflyer" description: "Connect AppsFlyer to Dataddo and sync its data to any dashboard, database, or data warehouse. No coding required." updated: 2026-07-09T08:20:47Z published: 2026-07-09T08:20:47Z canonical: "docs.dataddo.com/appsflyer" --- > ## Documentation Index > Fetch the complete documentation index at: https://docs.dataddo.com/llms.txt > Use this file to discover all available pages before exploring further. # AppsFlyer **AppsFlyer** is a mobile attribution and marketing analytics platform that helps businesses track and optimize their app marketing campaigns. It provides insights into user acquisition, engagement, and ROI, enabling marketers to make data-driven decisions and improve their app performance. Refer to our website for [the list of metrics and attributes available in Dataddo](https://www.dataddo.com/connector/apps_flyer). Refer to AppsFlyer's official documentation to see [all available endpoints from the AppsFlyer API](https://dev.appsflyer.com/hc/docs/api). ## Authorize Connection to AppsFlyer ### In AppsFlyer To authorize your AppsFlyer account, you will need **an API token**. 1. In your AppsFlyer account, click on your profile icon (email address) and navigate to the **Security center** page. 2. In the **AppsFlyer API tokens** section, select **Manage your AppsFlyer API tokens**. 3. Click on **+ New token**, enter a name (e.g. Dataddo), and select **API token type**. 4. Create the **API Token** and copy the value. Please make sure to use the token as soon as possible as it will be available only for 30 minutes. After that, you'll have to create a new token. ### In Dataddo 1. On the **Authorizers** page, click on [Authorize New Service](https://app.dataddo.com/service/new) and select AppsFlyer. 2. Provide your AppsFlyer **API Token**. 3. Rename your ***authorizer*** for easier identification and click on **Save**. ## Data Coverage AppsFlyer exposes the following datasets. Each dataset maps to a table you can extract. Example fields are a representative sample; each dataset returns more columns. | Dataset | Description | Example fields | Date range | | --- | --- | --- | --- | | Raw Data - In-App Events | | Ad, Ad ID, Ad Type, Adset, Adset ID, Advertising ID (+75 more) | Yes | | Raw Data - In-App Events Organic | | Ad, Ad ID, Ad Type, Adset, Adset ID, Advertising ID (+75 more) | Yes | | Raw Data - Installs | | Ad, Ad ID, Ad Type, Adset, Adset ID, Advertising ID (+75 more) | Yes | | Raw Data - Installs Organic | | ATT, Ad, Ad ID, Ad Personalization Enabled, Ad Type, Ad Unit (+120 more) | Yes | | Retargeting - Conversions | | Ad, Ad ID, Ad Type, Adset, Adset ID, Advertising ID (+75 more) | Yes | | Retargeting - In-App Events | | Ad, Ad ID, Ad Type, Adset, Adset ID, Advertising ID (+75 more) | Yes | | Skan Performance Report | | AF Attribution Flag, ARPU, Ad, Ad ID, Adset, Adset ID (+22 more) | Yes | ## How Data Extraction Works Every dataset for this connector uses a relative date range: the source reads a relative window (for example "last 7 days"), and that window slides forward with the current date. Every run re-reads the window, so a range of "1 day ago" always pulls the previous day (D-1). Each run replaces the window's data rather than adding older history. To load records from before the window, run a full data re-sync with a wider range. See [Data Backfilling](/docs/data-backfilling). Set the relative date range when you create the source. ## Metadata Columns When you create a source, you can add these Dataddo metadata columns to the extracted data: - **dataddo_hash** - a fingerprint built from each record's key fields. It works as a natural key, so it is ideal for upserts (updating existing rows in your destination instead of creating duplicates). - **dataddo_extraction_timestamp** - the date and time the row was extracted. Use it to track how records change over time, for example to build slowly changing dimensions. ## How to Create an AppsFlyer Data Source Creating a data source takes you through six steps, shown in the progress bar at the top of the wizard. Each step is explained below. **1. Pick the connector** On the **Sources** page, click **Create Source**, then select the connector from the catalog. Use the search bar or the category tabs if you do not see it right away. You can rename the source at any time using the pencil icon next to its name. **2. Select the dataset** A dataset defines the shape of your data: which fields you get and how they relate. Select the dataset you want; you can still fine-tune the exact fields later. - Each dataset has a short description of what it contains. Use the search box to find a dataset, attribute, or metric by name. - The panel on the right previews the selected dataset's fields. For each field you can see its **data type**, whether it holds **sensitive** data (personal fields such as name or email are flagged), and which **other datasets it links to**, so you can see how the datasets relate. **3. Choose the account** This step selects what Dataddo reads from. - **Authorizer:** Select an account you have already authorized from the drop-down. If you have none yet, choose **Add new account** and follow the prompts. If no authorizer is selected, Dataddo asks you to authorize before you continue. - **What to extract from:** Select the exact entity you want to pull data from. Depending on the service this may be labelled an account, property, profile, workspace, or similar, sometimes with a sub-level to choose as well. - **Multiple accounts:** To pull the same data from every entity you can access, turn on **Automatically collect data from all ...**. This is multi-account extraction. Leave it off to choose them by hand. **4. Refine the attributes and metrics** The dataset already sets the structure. Here you fine-tune it: tick or untick the specific attributes and metrics you want to keep, and use the search box to find a field quickly. Click **Test on Sample Data** at any point to preview the result before you continue. **5. Add metadata columns (optional)** Two optional columns help your destination handle the data. - **Dataddo Hash (Include Row Hash):** a fingerprint built from the columns you pick. It works as a natural key, so your destination can deduplicate rows and run upserts instead of creating duplicates. Turn it on, then select the columns that uniquely identify a row. - **Dataddo Extraction Timestamp:** the time each row was extracted. Use it to watermark the data, for example to build slowly changing dimensions or to track when a value last changed. **6. Set the schedule** Decide how often Dataddo runs the extraction. - **Frequency:** how often the pipeline runs, for example daily. Click **Show advanced settings** to also set the exact hour and minute (UTC). - **Date range:** the relative window each run extracts, for example "Yesterday". The window moves forward on every run. - **Historical data:** a new source starts from the current window. To load older data, run a [full data re-sync](https://docs.dataddo.com/docs/data-backfilling) after the source is created. - **Allow Empty Data Extractions:** when on, a run that returns no data records zero rows instead of failing. Turn it on if the source can legitimately have periods with no data. Click **Save**. Your data source is ready. ## Troubleshooting ### Data Preview Unavailable No data preview when you click on **Test Data** might be caused by an issue with your ***source*** configuration. The most common causes are: - **Date range**: Try a smaller date range. You can load the rest of your data afterward via [manual data load](https://docs.dataddo.com/docs/data-backfilling). - **Insufficient permissions**: Please make sure your authorized account has at least admin-level permissions. ## Related Articles Now that you have successfully created a ***data source***, see how you can connect your data to a dashboarding app or a data storage. **Sending Data to Dashboarding Apps** - [Simple Data Integration to Dashboards](https://docs.dataddo.com/docs/simple-data-integration-to-dashboards) - [Data Backfilling to Dashboarding Apps](https://docs.dataddo.com/docs/data-backfilling-to-dashboarding-apps) **Sending Data to Data Storages** - [Batch Ingestion to Data Warehouses](https://docs.dataddo.com/docs/ingestion-to-data-warehouses) - [Data Backfilling to Storages](https://docs.dataddo.com/docs/data-backfilling-to-storages) **Other Resources** - [Troubleshooting](https://docs.dataddo.com/docs/troubleshooting) - [Extraction Logs](https://docs.dataddo.com/docs/extraction-logs) - [Data Duplication](https://docs.dataddo.com/docs/data-duplication)