Incremental approach involves migrating parts of the database in small increments without causing downtime. This allows for continuous availability of the system while the migration is in progress, making it suitable for large databases where downtime must be minimized.

Sharding involves horizontally partitioning data across multiple databases or servers. It's an effective strategy for managing large datasets by distributing the load across multiple resources, thus enabling incremental migration without overwhelming a single database.

Migration tracking tools help monitor and manage changes to the database schema and data during migration. They provide visibility into the migration process, allowing teams to track changes, identify issues, and ensure the integrity and consistency of the database throughout the migration.

When migrating a large database to a new platform, ensuring data integrity and minimal service interruption is crucial. Transactional consistency and rollback procedures are vital to handle any errors or interruptions during the migration process, ensuring that the data remains consistent and intact. This includes implementing mechanisms to rollback changes in case of failures to maintain data integrity.

In situations with a large database and high transaction volume, employing strategies like data partitioning and parallel processing can help manage migration without disrupting ongoing operations. These techniques distribute the workload across multiple resources and allow concurrent processing, minimizing downtime.

Phased migration for a large database may be necessary when upgrading database software versions while preserving existing data. This approach involves breaking down the migration process into smaller, manageable phases, such as testing compatibility, migrating subsets of data, and gradually transitioning services to the new platform.

The common first step in addressing breaking changes after an Entity Framework update is to review the release notes. This helps in understanding the changes made in the new version and how they might affect the existing codebase.

The Entity Framework feature that allows for the identification of potential breaking changes in queries is query diagnostics. Query diagnostics provide insights into the performance and behavior of queries, helping developers identify any issues or inefficiencies.

Unit tests play a crucial role in identifying potential issues that may arise due to breaking changes in Entity Framework updates. They help validate the behavior of the application and ensure that existing functionality remains intact. While ensuring backward compatibility is important, it is the unit tests that actively identify and address issues, making option 2 the correct choice.

Entity Framework's backward compatibility ensures a smooth migration path to new versions by allowing existing applications to continue functioning without major modifications. It reduces the impact of breaking changes by maintaining compatibility with previous versions, making option 1 the correct choice.