Oracle Data Platform Foundations Associate (2025) — Lesson 2: Data ingestion, batch and streaming integration, replication, transformation, Data Integration, GoldenGate, object storage, and lakehouse patterns

Learning Objectives

• Assess the dependencies, controls, and expected business outcome for Data ingestion, batch and streaming integration, replication, transformation, Data Integration, GoldenGate, object storage, and lakehouse patterns.
• Demonstrate the workflow and leave reproducible evidence in source-to-platform data-flow diagram and ingestion validation.
• Recommend a preventive improvement after resolving a replication design preserving rows but losing the ordering needed by downstream processing.

Detailed Topics

• Data ingestion
• Batch and streaming integration
• Replication
• Transformation
• Data Integration
• GoldenGate
• Object storage
• And lakehouse patterns
• Decision ownership, prerequisites, and acceptance evidence for Data ingestion, batch and streaming integration, replication, transformation, Data Integration, GoldenGate, object storage, and lakehouse patterns
• Relevant access, data, integration, performance, recovery, and support considerations

Instructor Demonstration

Using realistic data and role assignments, the instructor demonstrates Data ingestion, batch and streaming integration, replication, transformation, Data Integration, GoldenGate, object storage, and lakehouse patterns. Key checkpoints are captured in source-to-platform data-flow diagram and ingestion validation, with discussion of alternatives, side effects, and the conditions required for an acceptable result.

Hands-On Exercise

Build or execute Data ingestion, batch and streaming integration, replication, transformation, Data Integration, GoldenGate, object storage, and lakehouse patterns for a defined business case. Introduce the condition “a replication design preserving rows but losing the ordering needed by downstream processing,” inspect the resulting evidence, correct the cause without discarding the audit trail, and document the final state in source-to-platform data-flow diagram and ingestion validation.

1. Map the current process and mark where the expected result can first be observed.
2. Run the scenario while preserving timestamps, job identifiers, and relevant output.
3. Apply a reversible correction only after evidence supports the cause.
4. Retest the original case plus an unaffected case and update the handover record.

Scenario Analysis

• Clarify the business trigger, affected roles, required records, and acceptable result for Data ingestion, batch and streaming integration, replication, transformation, Data Integration, GoldenGate, object storage, and lakehouse patterns.
• Compare at least two implementation or correction choices and identify which evidence would distinguish them.
• Decide how source-to-platform data-flow diagram and ingestion validation should be reviewed, approved, retained, and handed to the next owner.

Troubleshooting Guidance

Avoid editing multiple settings at once when troubleshooting Data ingestion, batch and streaming integration, replication, transformation, Data Integration, GoldenGate, object storage, and lakehouse patterns. Preserve the failing example, review source-to-platform data-flow diagram and ingestion validation, and verify whether “a replication design preserving rows but losing the ordering needed by downstream processing” is the root cause. After remediation, repeat the transaction, confirm downstream effects, and record any monitoring action.

Validation Checklist

• The stated requirement for Data ingestion, batch and streaming integration, replication, transformation, Data Integration, GoldenGate, object storage, and lakehouse patterns is linked to an owner and an observable acceptance condition.
• Source-to-platform data-flow diagram and ingestion validation contains enough detail for another practitioner to repeat the check.
• Normal processing, one boundary condition, and the identified failure path have all been tested.
• Access, data integrity, downstream impact, rollback, and support ownership are recorded.

Lesson Outcome

Learners can apply Data ingestion, batch and streaming integration, replication, transformation, Data Integration, GoldenGate, object storage, and lakehouse patterns, explain the decisions made, and defend the result with source-to-platform data-flow diagram and ingestion validation.