Writing custom Spark jobs in Databricks to compare dataset snapshots across environments, triggered as part of the deployment pipeline.

Utilizing Azure DevOps to run data validation scripts post-deployment, with manual interventions required to address inconsistencies.

Leveraging Azure Data Factory to orchestrate data movement and validation jobs between environments, using checksums for consistency checks.

Implementing a blockchain-based ledger within Databricks to record and verify data consistency across environments automatically.