How to Resolve Data Differences Quickly with dbForge Data Compare for SQL ServerWhen databases drift from each other, resolving data differences quickly is critical to maintaining application stability, ensuring reporting accuracy, and avoiding business disruptions. dbForge Data Compare for SQL Server is a specialized tool designed to detect, analyze, and synchronize data differences between SQL Server databases and backups with speed and precision. This article walks through setup, comparison strategy, practical workflows, and best practices so you can resolve data discrepancies fast and safely.
What dbForge Data Compare does (brief overview)
dbForge Data Compare for SQL Server compares table data between two databases (or a database and a backup/script) and generates a synchronization script to make target data match the source. It supports filters, key mapping, row-level comparison, comparison reports, and safe previewing of synchronization actions.
Key capabilities
- Detects row-level differences (insert, update, delete).
- Generates synchronization scripts you can review and run.
- Supports comparison of large datasets with performance optimizations.
- Allows filtering and mapping (table/column selection, custom keys).
- Provides detailed reports and a visual grid for easy inspection.
Preparation: before you compare
- Confirm objectives
- Decide which database is the source (authoritative) and which is the target.
- Define acceptable downtime and whether synchronization must be transactional.
- Back up target database
- Always take a full backup of the target (or at minimum of critical tables) before applying changes.
- Review permissions
- Ensure your login has SELECT on source tables and appropriate permissions (INSERT/UPDATE/DELETE or db_owner) on the target if you plan to apply changes.
- Identify comparison scope
- Choose which tables and columns to include.
- Consider excluding audit columns (timestamps, last_modified_by) if they naturally differ.
Step-by-step: comparing and resolving differences
- Launch dbForge Data Compare and create a new project
- Start a new comparison project and provide the connection details for your source and target SQL Server databases.
- Select objects to compare
- In the object selector, tick the tables you need. Use filters to limit to specific schemas/tables or to exclude transient/audit columns.
- Configure comparison options
- Choose comparison method (by primary key, unique key, or custom key).
- Enable row-level comparison options and set comparison timeout or batching for large tables.
- Run the comparison
- Click Compare. dbForge scans the selected tables and presents results grouped by table, showing counts of inserts, updates, and deletes.
- Review results in the results grid
- The results grid shows:
- Rows only in source (to be inserted into target)
- Rows only in target (candidates for deletion)
- Rows with differing column values (to be updated)
- Use the detailed row view to inspect column-level differences and change history columns.
- Customize synchronization actions
- Select which differences to include in synchronization. You can:
- Exclude specific rows or columns from updates.
- Convert delete actions into inserts or mark rows for manual review.
- Map non-identical keys or columns when schemas differ.
- Generate and preview synchronization script
- Generate a T-SQL synchronization script. dbForge shows the exact INSERT/UPDATE/DELETE statements it will run.
- Use the preview to review the SQL, check for potential data loss, and ensure correct WHERE clauses and keys.
- Execute synchronization safely
- If possible, run the script in a transaction or on a staging environment first.
- Execute the script from within dbForge or in SQL Server Management Studio (SSMS) after further review.
- Monitor for errors and re-run comparison to confirm all differences resolved.
Advanced workflows and tips for speed
- Use table partitioning and compare one partition at a time for huge tables.
- Enable batching: compare and sync in smaller chunks (e.g., 10k rows) to reduce locks and transaction log growth.
- Use filtered comparisons to focus on recent data (WHERE last_modified > ‘2025-01-01’).
- Turn on parallel processing where possible to utilize multiple CPU cores.
- For very large environments, perform a checksum/hash-based pre-scan to quickly detect unchanged rows and skip them during full comparison.
Handling special cases
- Schema differences: If column names or data types differ, use column mapping and type conversion options or create a shim view in the target to normalize columns for comparison.
- Identity columns: When synchronizing inserts, ensure correct identity_insert settings are used or strip identity columns from synchronization and reseed as needed.
- Referential integrity: Disable foreign key checks during mass sync only if you can re-validate and re-enable constraints afterward; otherwise synchronize parent tables first.
- Conflicting concurrent changes: Use row-versioning (snapshot isolation) or perform comparison during a maintenance window to avoid race conditions.
Safety checklist before applying changes
- Backup target database.
- Validate synchronization script in a dev/staging environment.
- Ensure transaction log has sufficient space for the changes.
- Run comparisons in read-committed snapshot isolation or during low activity to reduce blocking.
- Confirm rollback plan (restore from backup or run reverse synchronization script if needed).
Example scenario: syncing a customer table
- Scope: Source = ReportingDB.customers (authoritative), Target = OLTP.customers.
- Filter: Exclude last_login and last_reported_at columns.
- Key: Use customer_id as primary key.
- Run compare: 5,000 rows updated, 200 new rows, 15 rows deleted.
- Preview: Verify UPDATE statements affect only changed fields and WHERE clauses use customer_id.
- Execute: Apply script in a single transaction with a 5-minute maintenance window, then re-run comparison to confirm zero differences.
Reporting and auditability
dbForge Data Compare can generate comparison reports and save projects for repeatable operations. Keep comparison results and generated scripts in version control or an audit log for traceability. Record the time, user, and reason for each synchronization to support compliance and troubleshooting.
When to automate
Use dbForge command-line or scheduled projects when differences are predictable and reconciling regularly is required (e.g., nightly consolidation from regional databases). For production-critical merges, avoid fully automated destructive syncs without a manual review step.
Conclusion
Resolving data differences quickly with dbForge Data Compare for SQL Server is achievable with careful preparation, the right comparison settings, and safe execution practices. By using filters, batching, previews, and backups, you can minimize risk and restore data consistency efficiently.
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