![]() ![]() ![]() For example, to reduce the number of rows processed in the dataflow, you can use the filter condition in a sfdcDigest transformation. Structured filter: Can be defined using the JSON syntax. For each instance of sfdcDigest, you can use two types of filters - structured and advanced. This means filtering is only possible for primary key fields. While sfdcDigest transformations can extract data from Salesforce Big Objects, incremental extract isn’t supported. I suggest using the filter transformation node when the data sync is enabled instead of filters on sfdcDigest Node. In this case, you need to manually update the SFDC_LOCAL connection filter. If you delete a filter from a SfdcDigest transformation, the SFDC_LOCAL connection filter for that connected object won’t change. Please note: When you add SfdcDigest node advanced filter with Data Sync enabled, that filter is added automatically to the SFDC_LOCAL connection for that object. If you add an invalid filter, the dataflow will fail during run time. If you configured SfdcDigest Transformation for incremental extraction, the filter only applies to data extracted during the incremental run - Tableau CRM doesn’t apply the filter to records that were previously loaded into the dataset. Since sfdcDigest transformations extract all records for which the filter is true, filtering at this level will also enhance your dataflow performance. To extract only the required data from Salesforce Objects, a filter can be added to sfdcDigest Transformation. This transformation runs a Salesforce Object Query Language (SOQL) query to extract data from a Salesforce Object. ![]() In the example below, we applied this filter to the Account Name field to pull the data you need.Ī sfdcDigest transformation can be used to generate a dataset based on Salesforce Objects. Please note: Salesforce Local Objects filters utilize Condition Expression Syntax (WHERE Clause). Plus, adding a filter to the source object will impact all Dataflows involved. (Exclude unnecessary or sensitive data from syncing to Tableau CRM with data sync filters.) Filters run on the source object and speed up your data sync by pulling only the data you need into Analytics. In your sync setting, you can add a filter to the connected Salesforce Object. To restrict Salesforce records from being loaded into any dataset, add a filter transformation to the connected object. (In this situation, complex filters in a sfdcDigest transformation impact global filters.) To reduce the amount of data processing downstream, add a filter transformation as early as possible in the dataflow. If you’re looking to filter a stream of data loaded into a dataset, I recommend adding a filter transformation to the Dataflow. To provide clarity, let’s go over some specific examples. Quite often, it’s tricky to know where and how to use this filter - especially when multiple people are using the same Dataflow or Dataset. (You can use sfdcDigest transformation filters to reduce the number of rows processing in the Dataflow elsewhere.) Transformation level In addition, you can use this transformation to filter the records at any point in the Dataflow. To improve Dataflow performance, I recommend applying this filter closest to the point at which records are extracted. With this solution, you can define a filter condition that specifies which records to hold onto.ĭepending on your need, you can filter Salesforce Object records at different levels. Filter transformationĪ filter transformation - available in Tableau CRM Dataflow and New Data Prep recipes - is a great way to filter records from your existing dataset. I’ll also sketch out some scenarios to help you understand when and where to add each one. In this blog post, I’ll review the five most popular ways to transform data in Tableau CRM: filter transformation, sfdcDigest transformation, global filters, selection-based filter widgets, and filter interactions in bindings. Fortunately, there are several ways to quickly cut through the noise and filter your data before you feed it into the Tableau CRM engine. But sifting through mountains of information to isolate the data you need - and get rid of the data you don’t need - can be a cumbersome task. Tableau CRM (formerly known as Einstein Analytics) features a powerful AI engine that can turn your data into the predictive insights you need to drive ROI. ![]()
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