Oracle Data Integrator 12c: Integration and Administration Ed 3 — Lesson 3: Load Plans, CDC, Data Quality, and Big Data Integration

Technical Scope

• scenarios, load plans, serial/parallel steps, restartability, and exception branches
• journalizing, subscribers, CDC windows, consistency, and downstream consumption
• data quality checks, rejected rows, error tables, remediation, and reconciliation
• file, Hadoop, Spark, and heterogeneous integration patterns with operational controls

Instructor Demonstration

The instructor configures and validates scenarios, load plans, serial/parallel steps, restartability, and exception branches, then connects it to journalizing, subscribers, CDC windows, consistency, and downstream consumption. The demonstration captures the exact configuration, transaction, command, query, log, or screen evidence needed to prove intended behavior and the applicable control boundary.

Applied Workshop

Learners execute a guided lab focused on journalizing, subscribers, CDC windows, consistency, and downstream consumption and data quality checks, rejected rows, error tables, remediation, and reconciliation. The exercise introduces a realistic exception related to file, Hadoop, Spark, and heterogeneous integration patterns with operational controls and requires a reversible correction supported by before-and-after evidence.

1. Confirm scope, identities, data, dependencies, controls, and expected result.
2. Perform the product-specific configuration or analysis and record exact steps.
3. Validate functional behavior, security, data integrity, and operational observability.
4. Introduce the defined failure condition, diagnose the cause, and apply a safe correction.
5. Document rollback, acceptance evidence, unresolved risk, and ownership.

Validation Standard

Acceptance requires accurate handling of data quality checks, rejected rows, error tables, remediation, and reconciliation, traceable proof for scenarios, load plans, serial/parallel steps, restartability, and exception branches, and a repeatable recovery path for file, Hadoop, Spark, and heterogeneous integration patterns with operational controls. Results must be understandable to both implementers and operational support teams.

Failure Analysis

Learners compare symptoms, logs, state, dependencies, and recent changes when file, Hadoop, Spark, and heterogeneous integration patterns with operational controls occurs. The final diagnosis must separate the immediate fix from the preventive control.

Practical Outcome

The completed output is a tested load plans, cdc, data quality, and big data integration implementation or analysis, with validation evidence, troubleshooting notes, rollback guidance, and a concise operational handover.