Oracle GoldenGate 12c: Integrate Big Data Ed 1 — Lesson 2: Kafka, HDFS, Object Storage, Formats, and Delivery Configuration

Technical Scope

• Kafka producer settings, topics, keys, partitions, batching, and acknowledgments
• HDFS or file-system delivery, rollover, paths, permissions, and compression
• Avro, JSON, delimited, XML, Parquet awareness, and schema handling
• routing, naming, templates, serialization tests, and consumer validation

Instructor Demonstration

Demonstrate Kafka producer settings, topics, keys, partitions, batching, and acknowledgments within a real-time data pipeline publishing Oracle change events to a big data lake and streaming platform with schema control, delivery guarantees, observability, and replay, then capture the exact configuration, command, transaction, report, trace, or code evidence that proves the expected result.

Applied Workshop

Implement a controlled workshop covering HDFS or file-system delivery, rollover, paths, permissions, and compression and Avro, JSON, delimited, XML, Parquet awareness, and schema handling, then test a failure, exception, or business-control scenario involving routing, naming, templates, serialization tests, and consumer validation.

1. Confirm the environment, identities, data, dependencies, controls, and expected result.
2. Complete the product-specific configuration, development, analysis, or business-process task.
3. Capture commands, configuration, output, logs, transactions, reports, or test evidence.
4. Explain the operational, security, performance, data-quality, or business trade-off.

Validation Standard

The Kafka, HDFS, Object Storage, Formats, and Delivery Configuration result must be reproducible, traceable to approved inputs, and consistent with expected Oracle product or business-process behavior.

• The intended target, scope, and ownership are verified.
• The result is supported by reproducible evidence and expected-value checks.
• Security, performance, reliability, data quality, and support impact are reviewed.
• Unexpected behavior is investigated before acceptance or release.

Failure Analysis

Diagnose issues in Kafka producer settings, topics, keys, partitions, batching, and acknowledgments, HDFS or file-system delivery, rollover, paths, permissions, and compression, Avro, JSON, delimited, XML, Parquet awareness, and schema handling, or routing, naming, templates, serialization tests, and consumer validation without making an unverified production change.

Learners isolate the smallest proven cause, apply a reversible correction, rerun validation, and record the preventive action.

Practical Outcome

Complete a validated implementation or analysis for Kafka, HDFS, Object Storage, Formats, and Delivery Configuration and defend the result with documented evidence.