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DTM Data Comparer Review Features, Performance, and Tips

DTM Data Comparer is a Windows-based tool for comparing and synchronizing database table data across a variety of database systems. It’s designed for DBAs, developers, and analysts who need a reliable way to detect row-level differences, generate synchronization scripts, and ensure data consistency between environments (development, testing, staging, production). This review covers core features, performance, usability, and practical tips to get the most out of the tool.

Key features

  • Wide database support: native drivers and connectivity for major engines (MS SQL Server, MySQL/MariaDB, PostgreSQL, Oracle, SQLite, InterBase/Firebird, IBM DB2, and more).
  • Row-level comparison: detects inserted, deleted, and changed rows; highlights differing columns.
  • Synchronization script generation: creates SQL scripts to apply changes from source to target (or vice versa) with options for transactional execution.
  • Flexible matching keys: supports primary keys, unique keys, or user-defined column sets to match rows.
  • Filtering and data selection: compare entire tables or specific rows via WHERE clauses; exclude columns or use transformation/normalization rules.
  • Command-line support: automated comparisons and scheduled tasks via CLI.
  • Export/reporting: results exportable to HTML, XML, CSV, or SQL scripts for auditing and review.
  • Safe execution options: dry-run mode, transaction wrapping, and preview before applying changes.

Performance

DTM Data Comparer performs well on moderate-sized tables (thousands to low millions of rows) when network and database server resources are adequate. Performance depends on:

  • Network latency and bandwidth between source and target.
  • Indexing and key choices—proper keys drastically speed up comparisons.
  • Amount of data transferred—filters and column exclusions reduce payload.
  • Use of batching and transaction settings—smaller batches can reduce locking impact on production systems.

For very large datasets (tens of millions of rows), plan for:

  • Running comparisons during low-traffic windows.
  • Using indexed keys and server-side WHERE filters.
  • Comparing subsets iteratively instead of a single massive operation.

Usability

DTM Data Comparer has a straightforward GUI: choose source and target connections, select tables and matching keys, configure options, then run comparison. Highlights:

  • Clear color-coded result view (added/removed/changed rows and changed columns).
  • Inline previews and side-by-side row comparison.
  • Helpful wizards for connection setup and basic workflows.
  • Command-line options cover most GUI features for automation.

Limitations:

  • Windows-only desktop app—no native macOS or Linux GUI (CLI may be run via Wine or similar but not officially supported).
  • Some advanced transformations require manual scripting or preprocessing outside the tool.
  • Large-scale comparisons can consume significant memory; monitor system resources.

Safety and best practices

  • Always run a dry-run or generate scripts first; review before applying to production.
  • Wrap sync operations in transactions when possible; test rollback scenarios.
  • Backup target data before applying synchronization.
  • Use WHERE filters and process data in logical batches to minimize locks and reduce risk.
  • Test comparison and sync on a copy of production data to validate logic and performance.

Tips to maximize effectiveness

  1. Choose strong matching keys: use primary keys where available; if none exist, create deterministic composite keys or temporary unique identifiers.
  2. Exclude volatile columns: omit columns like timestamps or auto-increment counters when they’re not relevant to business-level differences.
  3. Normalize data where possible: ensure consistent casing, whitespace, and formats between source and target to avoid false positives.
  4. Use CLI and scheduling: automate recurring comparisons (nightly or weekly) and generate reports for auditing.
  5. Batch large tables: break comparisons by ranges (ID ranges, date ranges) to reduce memory and lock contention.
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