Run Data Sync and Recipes to Create and Refresh Datasets

Whether you use local Salesforce data or pull data from an external source, you must set up CRM Analytics to load the data, make it available to CRM Analytics, and keep it up to date.

To get the latest data, always run a data sync job to synchronize your data with the source before you run your recipe to load data into the dataset. To run these jobs on an ongoing basis, schedule syncs and recipes to run automatically and as frequently as needed. As you plan your strategy for loading and keeping data up to date, consider the CRM Analytics limits on your datasets and jobs.

• Run Data Sync to Synchronize Source Data in CRM Analytics
  Make sure that the data available to CRM Analytics matches the data in your source, whether its Salesforce or an external data source. You can manually run or schedule each sync.
• Run a Recipe
  Run a CRM Analytics recipe for the first time to create the dataset it defines. Run a recipe again to update the dataset with the latest synced data. You can run a recipe manually or on a schedule.
• Run a Dataflow
  Like recipes, you can run dataflows manually or schedule them to run automatically at regular intervals to create your datasets and keep the data up to date. You can also stop a dataflow while it’s running.
• Schedule Smarter with Priority Scheduling
  Priority scheduling for CRM Analytics recipes automatically manages your run queue. It prioritizes smaller and faster runs while ensuring that larger and longer runs are completed on time. Priority is automatically calculated based on factors such as historic runtime, dataset input size, and CSV file size. Priority scheduling is most helpful to smooth out occasional queue-time spikes. If you never or frequently see long queue times, then priority scheduling isn’t as helpful. Activate the feature in advance to manage your queue, not during a problem when your queue is already overloaded. This feature doesn’t increase your maximum number of concurrent runs.