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You can migrate legacy ETL to Palantir Foundry while aiming to keep data available, but Foundry does not guarantee a downtime-free migration. Continuity depends on how you capture source changes, align processing watermarks, switch consumers, and recover if the new path fails. The safer approach is to establish a correct baseline, validate the Foundry output against equivalent legacy input, and cut over only when your own service requirements are met.
Start by mapping what the legacy pipeline actually does
Before rebuilding jobs, document the behavior that must survive the move. A pipeline diagram alone is not enough: source systems and job names do not reveal how the system handles late data, corrections, or consumer expectations.
- List each source, ingestion cadence, dependency, target, and downstream consumer.
- Identify business keys and how the legacy path handles inserts, updates, deletes, duplicates, and late-arriving records.
- Record schema changes the pipeline has encountered, including added fields and changes to types or nullability.
- Capture current freshness expectations, run duration, failure handling, and reconciliation controls.
- Define the source versions or watermarks that will let you compare old and new outputs fairly.
These are local baseline measurements, not universal Foundry migration requirements. Your service expectations and recovery objectives should determine the cutover criteria.
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Foundry datasets are versioned through transactions, and transaction type affects what downstream work can safely assume. In file-based sync patterns, APPEND adds newly ingested data, while SNAPSHOT represents a replacement view. An update that overwrites existing files can violate the append-only assumption used by incremental processing and may require downstream work to run as a snapshot or batch instead. Check the actual connector behavior and source change semantics before selecting a transaction pattern.
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This distinction matters during migration: a source that periodically replaces a complete extract is not equivalent to one that only appends new records. If updates or deletes are meaningful, decide how the new path will represent them before relying on append-only processing.
Build a correct baseline before optimizing it
Palantir’s pipeline guidance presents batch processing as a sensible starting point in many cases, with incrementality added later when the amount of changed input makes the extra performance or latency worthwhile. A batch baseline helps establish whether the Foundry implementation reproduces the legacy business logic before you add transaction-aware behavior.
Batch can remain the right choice when inputs are snapshots, outputs need full replacement, data volumes are modest, or the logic depends on recalculating a complete aggregate. Incremental processing is not automatically more correct or less expensive overall: it adds development and maintenance work, and it must preserve the legacy meaning of changes, joins, aggregations, and replay.
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Decide whether each transform is a good fit for incremental processing
An incremental transform processes appended input changes since a prior build rather than recomputing the entire dataset. That can reduce repeated work, but only if the input transaction history and transform semantics support it.
| Decision factor | Batch is generally a better fit when… | Incremental is worth evaluating when… |
|---|---|---|
| Input pattern | Inputs arrive as full snapshots, are overwritten, or require full replacement. | Inputs are append-oriented and transaction history can be preserved. |
| Work per run | Most input data changes each run, or recomputation is already inexpensive. | A small portion changes per run and avoiding full recomputation is useful. |
| Business logic | The output depends on whole-history recalculation or semantics that the incremental transform cannot preserve. | The transform’s joins, aggregations, and other operations are supported in the required computation mode. |
| Operations | Simplicity and straightforward recovery outweigh potential latency or compute improvements. | The team can monitor transaction behavior and handle the additional maintenance burden. |
Pipeline Builder documents constraints that depend on transform type. For example, incremental join inputs have placement requirements, union inputs must use the same computation mode, and some aggregation or window operations apply to the current transaction rather than the full prior output. Confirm the exact transform behavior in the deployed Foundry version; do not assume a legacy full-refresh calculation has an incremental equivalent with identical results.
Plan for occasional SNAPSHOT input transactions as well. Recomputations can produce snapshots, so incremental logic must account for them rather than treating every future input as an append. Palantir’s guidance emphasizes understanding transaction behavior before taking on this added complexity.
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Make schema evolution explicit
Foundry schemas are metadata on dataset views and can evolve. A schema change therefore needs more than a successful write: check how the new shape affects the transform, downstream datasets, consumers, and any Ontology mappings.
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Keep incremental output declarations aligned
For Python incremental transforms, the schema used to read prior output must match the actual output types, nullability, and column order. Foundry stores columns as nullable, so declaring a previously written column non-nullable when reading prior output can produce a schema mismatch. Preserve deliberate field ordering when writing output, and test old and new schema versions rather than validating only a clean first run.
Assess Ontology changes separately
Dataset schema evolution and Ontology object type changes are related but distinct. Changes to an object type’s property type, backing datasource, or primary key can be breaking. Test representative old and new dataset schemas against downstream transforms, consumers, and object mappings before production cutover.
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Reconcile legacy and Foundry results on equivalent inputs
A comparison is meaningful only when both paths represent the same source state. Align the source version or watermark first; otherwise, differences may reflect timing rather than a migration defect. The following is an engineering reconciliation method, not a Palantir-prescribed algorithm.
- Choose the comparison boundary. Record the source versions or watermarks used for each run and verify that late-arriving data is treated consistently.
- Compare coverage. Check row counts and business-key coverage, then identify keys missing from either output and keys present more than once.
- Compare values. For matching business keys, compare important fields and aggregate control totals for measures that matter to the business.
- Exercise change cases. Test inserts, updates, deletes, duplicates, late data, nulls, and schema additions where those cases apply.
- Assign exceptions. Set tolerances and ownership from business rules. Record discrepancies, their resolution, and the validation evidence used for the cutover decision.
There is no universal acceptable tolerance: for some fields an exact match is required, while other measures may permit a documented difference. Decide that before reviewing results, not after seeing a mismatch.
Operate builds and schedules with failure semantics in mind
A Foundry build coordinates jobs that compute dataset versions. Jobs without dependencies can run in parallel. Build setup validates inputs and opens output transactions with build locking, but a failed build does not necessarily mean no output was written. If a job fails, directly dependent jobs and their output transactions are terminated; jobs that completed earlier may already have written data. During investigation, inspect committed dataset states and validation results individually rather than treating the whole build as an all-or-nothing event.
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Schedules trigger builds and can target datasets, exclude resources, and use single, full, or connecting build types. Palantir’s scheduling guidance favors simple schedules, one schedule per dataset, connecting builds where practical, retries, and reserving force builds for raw Data Connection syncs.
Two operational details are easy to misread:
- If a schedule triggers while a prior run is active, it waits for that earlier run to finish.
- A schedule run marked “succeeded” means the build was successfully initiated; it does not prove the build itself finished successfully.
Monitor the build outcome and dataset freshness as separate signals. A schedule history entry alone is not sufficient evidence that consumers have received a correct, current result.
Cut over with a defined rollback route
Run the legacy and Foundry paths in parallel long enough to cover ordinary schedules and known edge cases. Keep source capture and the previous consumer route available until reconciliation, freshness, and failure handling meet agreed criteria. Then switch consumers in a controlled step, observe actual build states and output transactions, and document how to restore the previous route.
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