You can link and harmonize massive amounts of data with Salesforce Data Cloud to provide scalable, personalized experiences for your consumers. It is imperative for Salesforce architects to understand the operation of Data Cloud. Data is becoming more palpable and critical than ever as businesses scramble to implement generative AI. I’ll go over five essential Data Cloud ideas that architects should be aware of in this article. Let’s dive in.
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1. A distinct kind of database is used to store data from the cloud.
Because the storage layer of Data Cloud is different from the relational database we are accustomed to dealing with, it can accommodate petabyte-scale data. A data lakehouse is where Data Cloud keeps all of its data. This data is really kept in S3 buckets using a Parquet file format. In contrast to the row-oriented CSV data format, which is intended for small amounts of complicated data, Apache Parquet uses a columnar file storage structure. We use Apache Iceberg, an abstraction layer between the actual data files and their table-forming organization, on top of the columnar storage. Iceberg supports supports data processing technologies like Apache Spark, Apache Presto and ultra-performant query services like Amazon Athena and Amazon EMR. The Data Cloud data lakehouse’s administration of record-level modifications and SQL queries is made possible by the combination of all of these technologies. The supplementary technology that underpins Data Cloud is shown in the diagram above.
2. A new collection of objects is used by Data Cloud.
Unstructured and structured data are supported by Data Cloud, as you may have heard. That is accurate. However, the transformation of this data to enforce structure is what gives Data Cloud its strength. You should familiarize yourself with three new categories of objects to facilitate the transformation process.
Data Model Objects are what are represented on the Customer 360 Data Model. Are you curious about the items in the Data Model? This is an excellent starting point: Customer 360 Data Model for Data Cloud.
3. Industry standard formats are utilized by data clouds.
Recall our conversation on Parquet and Apache Iceberg? These formats are accepted by the industry. Because Parquet is an open-source format supported by Snowflake and other cloud providers, we used it. Nevertheless, we also made a contribution to the open source community. Although batch procedures are the primary purpose of traditional data lakehouses, we have integrated features that enable large-scale batch and streaming events. Another community-driven open-source format is Iceberg.
We are able to design features like live query functionality—which enables other data lakes, like Snowflake, to query the data in DMOs without physically transferring or copying the data—because we are utilizing these industry-standard formats.
4. You may use Platform capabilities to interface with Data Cloud data.
Why would you choose to create your own solution vs using Salesforce when we have access to so much open-source technology? We can act on this hyperscale data using the features we are accustomed to from the Salesforce Platform, for starters, since we are mapping all of this data to our metadata structure and transferring that metadata structure back to the lakehouse. The data that underpins all the functionality, including identity resolution, segmentation, activation, and, of course, Einstein services, is modeled as Data Model Objects (DMOs). Additionally, you can see your Data Cloud data directly from the Salesforce Platform’s core using pre-built capabilities like the Customer Data Profile, which eliminates the need for you to create a bespoke user interface.
5. Returning Data Cloud Data to the transactional database is an option.
Although data from the cloud does not always return to the source system (like Sales or Service Cloud), you may use some components to see data from the cloud alongside transactional database data. For instance, a contact record allows you to view the segments that the contact is in. What happens if you need to send data from the Data Cloud back to the Marketing, Sales, or Service clouds? Make use of Data Actions, which let you communicate via Flow or Platform Events.
In summary
As a customer’s most trusted technical resource, architects need to really grasp how our products function so they confidently advise on the ideal solution. I hope this blog has given you some insight into Salesforce Data Cloud and that you will keep learning more about this fascinating technology. This is especially true as it becomes more and more important to combine data from many sources to build a comprehensive picture of your customer in order to advance AI capabilities.
