Export all Talend jobs, configurations, and metadata. For each job, document: name, source, target, transformations, schedule, dependencies, and error handling. Create a spreadsheet categorizing jobs by complexity (simple ETL, complex transformation, on-premises sources, cloud sources).
⚠️ Watch Out For:
- Talend jobs can be nested (parent/child relationships)—trace these dependencies carefully
- Custom components and libraries may not be well-documented—interview team members
Sort jobs into three buckets: (1) Simple cloud ELT (ideal for Fivetran), (2) Cloud ELT + light transformation (Fivetran + dbt), (3) Complex or on-premises (keep in Talend). Focus migration effort on Bucket 1 jobs for highest ROI.
⚠️ Watch Out For:
- Overestimating what can move to Fivetran leads to scope creep—be conservative
- Some jobs may have implicit business logic not reflected in the job design—investigate
Sketch the post-migration architecture: cloud sources → Fivetran → warehouse → dbt. Identify which Talend jobs map to each layer. Define ownership and responsibilities (data engineering: Fivetran, analytics engineering: dbt). Document the architecture.
⚠️ Watch Out For:
- Decomposing Talend's monolithic jobs into Fivetran + dbt requires rethinking responsibilities
- Data quality checks may need to move from Talend jobs to dbt tests
Create Fivetran account and workspace. Set up warehouse connections (Snowflake, BigQuery, Redshift, etc.). Create team members with appropriate roles. Configure SSO if required. Test warehouse connectivity.
⚠️ Watch Out For:
- Warehouse credentials must have schema/table creation permissions—verify this early
- Network access rules (firewall, VPN) must be updated for Fivetran's IPs
Select the simplest Talend job (single source, single target, minimal transformation). Create the equivalent Fivetran connector. Configure table selection and sync mode. Run a test sync. Compare outputs with the original Talend job.
⚠️ Watch Out For:
- Fivetran's table selection is more conservative than Talend's—review what's being synced
- Column ordering and data types may differ—test sample data carefully
For jobs with light transformation, create dbt models that build on Fivetran-loaded tables. Rewrite Talend transformations as dbt SQL. Set up dbt to run after Fivetran syncs (via webhook or orchestrator). Test dbt output matches original Talend transformations.
⚠️ Watch Out For:
- Talend's visual transformation designer doesn't translate directly to dbt SQL—requires careful rewriting
- dbt's dependencies are explicit—ensure correct execution order
Progressively migrate remaining Talend jobs to Fivetran (Bucket 1 and 2). Start with simple ones. For each, validate that outputs match original Talend job. Document any jobs that proved too complex to migrate.
⚠️ Watch Out For:
- Talend jobs with complex branching or error handling logic may require orchestration tool support
- Late-arriving dimension patterns or slowly changing dimensions require special handling in dbt
Once Fivetran connectors are validated, disable corresponding Talend jobs. Keep Talend running for Bucket 3 jobs (those not migrated). Archive Talend job definitions for reference. Document which Talend capabilities are still in use.
⚠️ Watch Out For:
- Don't delete Talend jobs immediately—archive them for 2-4 weeks for recovery if issues arise
- Talend licenses may have other use cases—verify you're not removing needed capabilities
Keep both Talend and Fivetran pipelines running in parallel for 1-2 full cycles. Compare record counts, data accuracy, and timing. Validate that downstream analytics and dashboards match expectations with Fivetran data.
⚠️ Watch Out For:
- Timing mismatches between Talend and Fivetran schedules complicate comparison—align temporarily
- Small data discrepancies (NULL handling, data type precision) often surface during parallel runs—investigate thoroughly
Finalize cutover by removing Talend jobs from critical path. Update SLAs and alerting for Fivetran. Monitor first 2 weeks of production closely. Review Fivetran costs (MAR-based) and optimize table/column selection if needed. Document lessons learned.
⚠️ Watch Out For:
- First month of Fivetran costs can be high—review data volume and optimize early
- Team members may still reference Talend—update wikis and runbooks proactively
