Portability, Migration and Data Discovery
This topic covers business mapping, portability, data residency, staging, cataloging, profiling, discovery, migration plans, Transfer Appliance, Datastream, and Database Migration Service.
How to study for Google Professional Data Engineer
Treat each item as a data workload decision: identify source, sink, velocity, schema, governance, storage pattern, processing mode, and operational risk.
Core concepts
Concept 1
Portability, Migration and Data Discovery questions test data engineering design decisions across ingestion, storage, analysis, automation, governance, and reliability.
Exam cue: Identify the source, sink, processing mode, data model, access pattern, freshness requirement, and governance boundary.
Concept 2
The best answer maps data shape, velocity, quality, access pattern, compliance, processing model, and operations needs to the right Google Cloud service.
Exam cue: Choose the service pattern that satisfies batch, streaming, analytics, ML, storage, security, and operational requirements.
Concept 3
Eliminate answers that ignore schema evolution, late data, IAM, regional constraints, lineage, cost, quotas, or recovery behavior.
Exam cue: Prefer managed, observable, repeatable, secure, cost-aware, and fault-tolerant data pipelines when requirements support them.
Risk pitfalls and guardrails
Choosing a storage system without checking query pattern, latency, consistency, cost, and lifecycle requirements.
Guardrail: Avoid answers that ignore IAM, privacy, schema quality, late data, storage access patterns, query cost, quotas, or pipeline failure handling.
Treating streaming data like batch data when event time, windows, and late arrivals matter.
Guardrail: Avoid answers that ignore IAM, privacy, schema quality, late data, storage access patterns, query cost, quotas, or pipeline failure handling.
Ignoring data governance, privacy, monitoring, or automation until after the pipeline is built.
Guardrail: Avoid answers that ignore IAM, privacy, schema quality, late data, storage access patterns, query cost, quotas, or pipeline failure handling.
Memory anchors
Portability
Portability design helps data and applications move across environments, regions, or clouds when required.
Data Residency
Data residency defines where data must be stored, processed, or accessed.
Data Staging
Data staging temporarily holds data before validation, transformation, or loading.
Data Cataloging
Data cataloging records metadata so datasets can be discovered, governed, and understood.
Data Profiling
Data profiling analyzes structure, distribution, quality, and anomalies in datasets.
BigQuery Data Transfer Service
BigQuery Data Transfer Service automates loading data from supported sources into BigQuery.
Database Migration Service
Database Migration Service helps migrate databases to Google Cloud managed database services.
Transfer Appliance
Transfer Appliance moves large volumes of data to Google Cloud using physical hardware.
Datastream
Datastream captures and streams database changes for replication and integration.
Target State
A target state describes the desired data architecture, tools, governance, and operations model.
Checkpoint rule
Do the check-up only after you can summarize each concept in one sentence and identify one dangerous pitfall from memory.
Knowledge Check (after reading)
Short check-up to confirm understanding of this module.
Check-up Questions
Which Google Cloud service migrates large volumes of data from online sources like AWS S3 into Cloud Storage?
When would you use a Transfer Appliance rather than online transfer?
Answer all questions to submit.
Next step personalized recommendations
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Move forward only after this module is stable.
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