Oracle Big Data Fundamentals Ed 2 — Lesson 3: Distributed Processing, SQL on Big Data, Streaming, and Machine Learning Context

Technical Scope

• parallel processing, transformations, shuffle, fault tolerance, and resource use
• SQL engines, external tables, pushdown, and analytical query patterns
• stream processing, windows, event time, late data, and state
• feature preparation, model lifecycle awareness, and analytical validation

Instructor Demonstration

Demonstrate parallel processing, transformations, shuffle, fault tolerance, and resource use within a data platform team evaluating how high-volume clickstream, device, and transaction data should be ingested, stored, processed, governed, and served for analytics, then capture the exact configuration, command, transaction, report, trace, or code evidence that proves the expected result.

Applied Workshop

Implement a controlled workshop covering SQL engines, external tables, pushdown, and analytical query patterns and stream processing, windows, event time, late data, and state, then test a failure, exception, or business-control scenario involving feature preparation, model lifecycle awareness, and analytical 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 Distributed Processing, SQL on Big Data, Streaming, and Machine Learning Context 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 parallel processing, transformations, shuffle, fault tolerance, and resource use, SQL engines, external tables, pushdown, and analytical query patterns, stream processing, windows, event time, late data, and state, or feature preparation, model lifecycle awareness, and analytical 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 Distributed Processing, SQL on Big Data, Streaming, and Machine Learning Context and defend the result with documented evidence.