Analytics by Einstein
Since it enables users to link data from multiple platforms into one location and examine complex data quickly and simply, Einstein Analytics has grown in popularity as a business intelligence (BI) tool.
Work with Salesforce Objects using Einstein Analytics datasets
However, because this information is not included in salesforce fields, Einstein prevents us from running reports, workflows, or process flows based on the data in datasets.
We have recently encountered the need to combine data from Salesforce objects with datasets from Einstein Analytics. This is to populate Salesforce custom fields with certain important data so that campaigns, reports, and workflows may be executed using it.
Three applications for Einstein Analytics to employ SAQL
Apex class for SAQL Analytics REST API (to query the data)
SAQL Synopsis
Data from analytics datasets can be accessed via SAQL, a Salesforce Analytics Query Language. SAQL can be used for a variety of tasks, including the creation of sophisticated dashboards, data manipulation, and computations to add data to datasets. In our instance, we integrated it with (inserted into) the Salesforce objects and utilized it to retrieve the data from the databases.
An operation, an output stream, and an input stream are present in every SAQL statement. Statements must terminate with a semicolon and may span more than one line.
REST API for Analytics
The Analytics REST API allows programmatic access to analytics capabilities like datasets, dashboards, and lenses. To obtain the necessary information/data through these Analytics REST API, developers can create SAQL.
Using Apex Class to Query Analytics Data
The Chatter REST API serves as the foundation for the Analytics REST API, which adheres to its rules. With the aid of the Analytics Apex SDK’s query builder classes and execute Query method, we can produce a well-formed query in the Apex class. Creating and sending a SAQL query from the apex class to Analytics and receiving a JSON string result is really simple.
Obstacles
There aren’t many difficulties with this integration:
Large amounts of data to combine:
The heap size limit error occurs when a dataset has a lot of data in it or if the response has more data than 12MB.
In order to deal with this, we used the SAQ query parameters “limit” and “offset” to obtain the paginated analytics answer.
SAQL example using “offset” and “limit”:
Latest Version Id of the dataset:
As you can see, one of the necessary query input parameters that we pass through in every SAQL is “version id.” For SAQL to function as intended, it is imperative that the most recent version ID be given. The version id is dynamic and cannot be hardcoded into the code because it varies each time the dataset is altered. Using the custom settings and manually adjusting the value each time the version id changes is also laborious.
We have used an API to retrieve the dataset’s most recent version id to get around this problem. In total, two API calls are made to Analytics: the first retrieves the dataset’s most recent version ID, and the second retrieves data from the dataset.
To obtain the dataset version id, an API request to Analytics must first be made with the following parameters completed:
Setup Connected App to offer safe access to REST API
Auth Provider was created to allow for limited access.
Authorization with named credentials (to prevent hardcoding the secret and API key)
Once the elements are all up, we can use named credentials to make the API call.
Exam group:
It was a little difficult to obtain the code coverage for the apex classes using connectapi.literaljson. Since most Chatter in Apex methods fail unless used in test methods designated @IsTest(SeeAllData=true), we were forced to use the seeAllData=true parameter.
The blog post above explains how to use REST APIs to integrate Einstein Analytics data into Salesforce objects and how to use Salesforce Einstein Analytics for efficient business insights. Watch this space for updates.
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Overview of Analytics REST API
By using the Analytics REST API, you may programmatically access analytics capabilities like datasets, dashboards, and lenses.
By using the REST API for Analytics, you can:
- Directly submit inquiries to the Analytics Platform.
- Obtain access to imported datasets on the Analytics Platform.
- Make Analytics lenses and retrieve them.
- Make a backup of your Analytics dashboards, lenses, and dataflows, then restore them.
- Refer to Backup and Restore Analytics Assets with History API in Previous Versions.
- Run, plan, and synchronize connections, recipes, and dataflows for analytics. View Utilize
- REST APIs to Run, Schedule, and Sync Einstein Analytics Data Obtain XMD data.
- Obtain a list of versions of the dataset.
- Make analytics applications and retrieve them.
- Make Analytics dashboards, retrieve them, and update them.
- Obtain an application’s dependency list.
- Identify the functionalities that the user has access to.
- Work with and plan snapshots of Trend in Analytics reports.
- Modify synchronized datasets, sometimes referred to as linked objects.
- Obtain, add, edit, and remove “eclair” geospatial maps.
- Utilize data connectors in your work.
- Obtain or modify the recipe’s metadata.
- Find out if specific dataset versions and objects support sharing inheritance.
- Use the SmartDataDiscovery API to obtain Einstein Discovery predictions for Salesforce objects.
Conclusion:
Integrating Einstein Analytics data into Salesforce objects empowers organizations to derive valuable insights and enhance decision-making processes. By leveraging SAQL queries and the Analytics REST API, businesses can seamlessly merge data from Einstein Analytics with Salesforce objects, enabling comprehensive analysis and actionable insights. To streamline this integration process and maximize the benefits of Einstein Analytics, consider hiring experienced Salesforce Einstein Developers or engaging Salesforce Einstein Services.
