Migrating a large number of data pipelines from one tool to another can be a daunting task. With dependencies, execution schedules, and business-critical functions to consider, a structured approach is essential to ensure a smooth transition. In this post, we’ll outline a practical framework for planning and executing a large-scale migration of data pipelines.
1. Understand What You Have
Before starting the migration, document your existing data pipelines. If you haven’t already, this is an excellent opportunity to create an inventory that includes:
- Pipeline names and associated workflows
- Data sources and destinations
- Execution schedules
- Dependencies between pipelines
- Performance metrics (e.g., execution time, failure rates)
Why It Matters
A clear inventory ensures that no critical dependencies are missed and allows for better resource planning. It also provides a baseline for tracking progress throughout the migration.
2. Devise a Goal-Based Approach
Decide on a systematic way to order the migration of pipelines. The ordering can be based on different factors such as:
- Cost: Prioritising high-cost pipelines.
- Execution frequency: Prioritising frequently running pipelines.
- Performance: Prioritising long-running or inefficient pipelines
- Risk: Prioritising low-impact pipelines before tackling business-critical ones.
- Alphabetical (yes, really!): Sometimes, a simple ordering method like this helps maintain structure.
Why It Matters
A clear, goal-based migration order provides structure. It ensures that everyone understands why specific pipelines are being migrated, and what is coming next.
3. Communicate Progress Clearly, and Often
Migrations can take weeks or months, so regular communication is key. A goal-based approach allows progress to be broken into clear updates, such as:
- “All pipelines that were executed between 12 and 2pm have been migrated. We’ll tackle pipelines in the 10am -12pm window next.”
- “$200 of execution costs (out of $1000) are now running on the new system. We expect to migrate a further $100 of pipelines in the next fortnight.”
- “We’ve completed the migration for all pipelines with execution times over 45 minutes. We’ll now move on to the five pipelines that take 30+ minutes.”
Use your company’s standard communication tools, and don’t be afraid to over-communicate—repetition helps ensure the message gets across!
Why It Matters
Large-scale migrations require sustained momentum. Regular updates demonstrating progress will keep stakeholders engaged and reassured.
4. Build your pipelines side-by-side
When migrating, create the new pipelines to populate a separate schema (e.g. schema_new.table_a instead of schema_old.table_a). This parallel setup enables direct comparison between the new and old data, and provides a safe fall back if issues are discovered.
Why It Matters
This approach simplifies the accuracy checks and removes the pressure of having to switch pipelines immediately, which allows for a controlled transition. If needed, use views to present the new data as if it were in the original location.
5. Seize the Opportunity!
Migration is not just about replication—it’s an opportunity to make simple improvements. While moving pipelines, consider:
- Addressing technical debt and inefficiencies.
- Enhancing monitoring and alerting.
- Automating manual processes.
- Aligning with best practices in the new tool.
Why It Matters
Migrating to a new tool is a chance to fix issues and optimise pipelines rather than simply porting old inefficiencies. Ensuring best practices from the start will set you up for long-term success – you don’t want to fold a major restructure into your migration project, but making bitesize improvements as you go will set you up for the future.
Final Thoughts
While the prospect of migrating numerous data pipelines might seem daunting, remember that by adhering to a structured methodology that prioritises understanding, goal-setting, communication, validation and proactive improvement, you’re not just moving pipelines from one tool to another; you’re laying the foundation for a more agile, efficient, and robust data ecosystem, which will pay dividends long after the migration is complete.
