Salesforce Data-Architect Exam With Confidence Using Practice Dumps

Using an ETL tool to orchestrate the migration is the best strategy for this scenario, as it can handle the data model changes, the large volume of records, and the tight timeline. Writing a script to use the Bulk API (option C) is also possible, but it would require more coding and testing effort. Using Data Loader and Data Import Wizard (option B) is not suitable for migrating three million records, as they have limitations on the batch size and the number of records per operation. Using Salesforce CLI (option D) is also not recommended for large data migration, as it is mainly designed for development and testing purposes

Grouping batches by the AccountId field can improve the performance and avoid locking issues when loading Opportunity records using the Bulk API in parallel mode1. This is because the AccountId field is indexed and can be used to distribute the records evenly across batches

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.