When the time comes to migrate from your legacy SQL-based systems, Popdock provides a streamlined path to ensure your historical data is preserved and accessible. By moving your data into a data lake, you ensure it is ready for use with your new system without the overhead of maintaining old servers.
The following best practices will help you set up and execute a highly efficient data migration.
The Pre-Migration Strategy
Only Bring the Data You Need
While it is tempting to migrate every byte of data “just in case,” the most efficient migrations focus on necessity. Migrating only essential data saves storage space and significantly reduces the time required for the initial upload.
- Audit your databases: Identify and exclude test databases or obsolete historical archives.
- Evaluate third-party data: If you have data from a third-party extension that hasn’t been used in years, consider leaving it behind.
- Streamline: Less data means a faster migration and a cleaner data lake environment.
Maintain a Post-Migration Backup
Once you have completed your final migration, maintain a backup copy of all data migrated from SQL. This is a standard best practice for disaster recovery. If data becomes corrupted in the data lake or requires a refresh, having a SQL backup allows you to re-run the Popdock Data Lake Upload Tool quickly to restore your information.
Test Run Before Your Full Migration
Before committing to a full-scale migration, we recommend performing a mini test migration.
- The 3-5 List Rule: Select only 3–5 lists to send to the data lake initially.
- Verify Results: Ensure the data lands correctly and without errors.
- Assess Performance: Use this test to get an accurate estimate of how your full data set will behave during the move.
Execution and Performance
Timing the Migration
Data migration is not an instant process. The larger the data set, the more time it requires. We recommend running the final migration outside of normal operating hours. This ensures the process is not interrupted by network traffic or server maintenance, allowing it to run to completion smoothly.
Handling Large Data Tables
Legacy data can be massive, especially regarding General Ledger Entries or Posted Sales Transactions. These high-volume lists can sometimes cause performance lag during filtering or customization within Popdock.
Platform Specifics: Dynamics GP and SL
For Microsoft Dynamics GP and Microsoft Dynamics SL migrations, Popdock utilizes a two-stage migration process. Understanding the difference between how Lists and Tables are handled is key to maintaining a high-performance integration.
The Two-Stage Migration
Stage 1: Lists – These are your pre-defined, user-friendly data sets. When you run a List migration, Popdock automatically populates them into the Lists Connector for immediate use.
Stage 2: Tables – These are the raw, underlying SQL tables. Unlike Lists, the Tables Connector will remain empty even after a successful migration. This is a deliberate design choice.
Why the Tables Connector Starts Empty
Legacy systems like GP and SL contain thousands of raw tables, many of which are system-generated or redundant. If Popdock attempted to load every table automatically:
- It would trigger a massive metadata pull lasting several hours.
- It could render the connector unusable until the sync completes.
- It would clutter your workspace with unnecessary data.
The “As-Needed” Approach
To keep your Popdock environment fast and organized, do not add every possible table at once. Instead:
- Identify specific needs: Only pull in raw tables if you require data not already covered by a standard List.
- Add incrementally: If you only need three specific tables, you can add the list for just those three. You can add more lists in Popdock for additional tables later.
Have questions about utilizing Azure data lakes with Popdock? Feel free to contact our team if you want to learn more.
