Brandwatch

Brandwatch is a digital consumer intelligence platform that helps businesses analyze and understand online conversations, trends, and consumer behaviors. It offers tools for social media monitoring, market research, and brand sentiment analysis, enabling companies to gain insights and make data-driven decisions.

Refer to our website for the list of metrics and attributes available in Dataddo.

Refer to Brandwatch's official documentation to see all available endpoints from the Brandwatch API.

Connection Authorization

There are two ways to authorize your connection to Brandwatch, depending on the services that you use. Both will need an API key.

Make sure you have at least admin-level privileges and proceed based on your Brandwatch service:

• Brandwatch: The core platform focused on social listening and consumer intelligence, analyzing vast public online data to provide strategic insights into trends, sentiment, and market behavior.
• Brandwatch SSM: A specific product suite (formerly Falcon.io) dedicated to the daily operational tasks of a brand's social presence, including scheduling content, managing audience engagement, and analyzing owned-channel performance.

Authorize Connection to Brandwatch

In Brandwatch

1. Run the following command in your terminal/CLI:

   curl -X POST --data-urlencode 'password=[yourpassword]' \
     'https://api.brandwatch.com/oauth/token?username=[your@username.com]&grant_type=api-password&client_id=brandwatch-api-client'
   

   Make sure to replace [yourpassword] and [your@username.com] with your values.
2. The API key (access_token) will be provided in the JSON response of the call. Copy this value for later use in Dataddo.

In Dataddo

1. On the Authorizers page, click on Authorize New Service and select Brandwatch.
2. Fill in the following fields:
   1. Username: Your Brandwatch username.
   2. Password: Your Brandwatch API Key functions as a password to your username.
3. Rename your authorizer for easier identification and click on Save.

Authorize Connection to Brandwatch SSM

In Brandwatch SSM

1. In the bottom-right, click the Settings icon and select Organization admin.
2. From the left menu, navigate to the Integration marketplace page.
3. Switch to the API tab and click on + New API Key. You can have up to 5 API keys at a time.
4. Copy the API key value for later use in Dataddo.

In Dataddo

1. On the Authorizers page, click on Authorize New Service and select Brandwatch SSM.
2. Fill in the API Key.
3. Rename your authorizer for easier identification and click on Save.

Data Coverage

Brandwatch exposes the following datasets. Each dataset maps to a table you can extract. Example fields are a representative sample; each dataset returns more columns.

How Data Extraction Works

What each extraction pulls depends only on whether a dataset supports a date range (see the Date range column above):

• Date range supported (Yes): 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.
• No date range (No): every run pulls all currently available data.

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 a Brandwatch 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 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.

Limitations

Data Restrictions

Some data packs may impose restrictions on Mention data which can be extracted through the Consumer Research API. This can lead to discrepancies between data in your Consumer Research app UI and data retrieved by Dataddo.

For more specific information on your data packs, refer to Brandwatch's official documentation. The following sources are affected:

• Twitter
• News
• Lexis Nexis
• Mumsnet
• Sina Weibo

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.
• 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
• Data Backfilling to Dashboarding Apps

Sending Data to Data Storages

• Batch Ingestion to Data Warehouses
• Data Backfilling to Storages

Other Resources

• Troubleshooting
• Extraction Logs
• Data Duplication