Salesforce Data-Architect Exam With Confidence Using Practice Dumps

The recommended solution for the Cloud Kicks Data Architect to address the requirements is to create a custom object to track shipment requests and a child custom object to track shipment items. Implement an archiving process that moves data off-platform after three years. This solution would allow Cloud Kicks to store and manage their shipment data on Salesforce, and use Salesforce reports to analyze it. However, since Cloud Kicks expects a large volume of data over time, they should implement an archiving process that moves data off-platform after three years to avoid hitting the Org data storage limit and maintain optimal performance3. External objects are not a good option for this scenario, because they are stored off-platform in an external system, such as Heroku’s Postgres database, and they have limited functionality and performance compared to custom objects

The best design pattern to satisfy the requirement of accessing order data from an on-premise database with REST API access without consuming SF storage limits is to use SF Connect to virtualize the data in SF and avoid storage limits. SF Connect is an integration tool that allows users to access and integrate data from external sources using external objects. External objects are similar to custom objects, except that the data resides in another system and is accessed in real time via web service callouts. SF Connect supports various adapters to connect to different types of external data sources, such as OData, cross-org, or Apex custom adapter11. Migrate and persist the data in SF to take advantage of native functionality is not a good option because it would consume SF storage limits and require data synchronization between systems. Develop a bidirectional integration between the on-premise system and Salesforce is not a good option because it would be complex and costly to implement and maintain, and it would also consume SF storage limits. Build a UI for the on-premise system and iframe it in Salesforce is not a good option because it would not provide a seamless user experience and it would not allow users to search, report, or perform actions on the external data.

According to the Salesforce documentation, a hub-and-spoke model is an integration architecture pattern that allows connecting multiple Salesforce orgs using a central org (hub) and one or more secondary orgs (spokes). The hub org acts as the master data source and orchestrates the data flow between the spoke orgs. The spoke orgs act as the consumers or producers of the data and communicate with the hub org.

To orchestrate data between the hub org and spoke orgs, a data architect should use:

A middleware solution that extracts and distributes data across both the hub and spokes (option A). This means using an external service or tool that can connect to multiple Salesforce orgs using APIs or connectors, and perform data extraction, transformation, and distribution operations between the hub and spoke orgs. This can provide a scalable, flexible, and reliable way to orchestrate data across multiple orgs.

Developing custom APIs to poll the hub org for change data and push into the spoke orgs (option B) is not a good solution, as it can be complex, costly, and difficult to maintain. It may also not be able to handle large volumes of data or complex transformations efficiently. Developing custom APIs to poll the spoke orgs for change data and push into the hub org (option C) is also not a good solution, as it can have the same drawbacks as option B. It may also not be able to handle conflicts or errors effectively. Using a backup and archive solution that extracts and restores data across orgs (option D) is also not a good solution, as it can incur additional costs and dependencies. It may also not be able to handle real-time or near-real-time data orchestration requirements.