Google Cloud Professional Data Engineer Quick Facts (2025)

Exam Domain Breakdown

Domain 1: Designing data processing systems (22% of the exam)

Designing for security and compliance

• Identity and Access Management (e.g., Cloud IAM and organization policies)
• Data security (encryption and key management)
• Privacy (e.g., personally identifiable information, and Cloud Data Loss Prevention API)
• Regional considerations (data sovereignty) for data access and storage
• Legal and regulatory compliance

Summary: This section explores how to embed security and compliance into every layer of your data engineering work. You will focus on identity management through Cloud IAM, integrate encryption and key management practices, and implement data privacy controls through services such as Cloud DLP. Additionally, understanding how geographic sovereignty impacts data access and storage is crucial for guiding decision-making across global workloads.

Equally important, you will explore legal and compliance requirements that influence architecture and operations. Identifying and applying regulatory standards ensures that systems maintain integrity while meeting organizational and industry expectations. This builds resilience and trust into your data strategy by showing mastery of both technology and governance.

Designing for reliability and fidelity

• Preparing and cleaning data (e.g., Dataprep, Dataflow, and Cloud Data Fusion)
• Monitoring and orchestration of data pipelines
• Disaster recovery and fault tolerance
• Making decisions related to ACID (atomicity, consistency, isolation, and durability) compliance and availability
• Data validation

Summary: Here you will learn to balance system reliability with data quality. This begins with cleaning and preparing data for use, setting up monitoring, and tracking workflows across tools such as Dataflow, Dataprep, and Cloud Data Fusion. The knowledge gained ensures pipelines are efficient and resilient to unexpected issues.

Disaster recovery strategies, consensus choices around ACID compliance, and data validation requirements are emphasized to maintain durability and trustworthiness of insights. This ensures data workflows remain highly available, consistent, and reliable, even in dynamic production settings.

Designing for flexibility and portability

• Mapping current and future business requirements to the architecture
• Designing for data and application portability (e.g., multi-cloud and data residency requirements)
• Data staging, cataloging, and discovery (data governance)

Summary: This section highlights how to design scalable systems aligned with both present and future business needs. You will identify requirements and translate them into architectures that adapt to new workloads, markets, and demands. Flexibility and portability are emphasized with multi-cloud use cases and solutions that respect data residency requirements at a global level.

Building in tools for data governance such as staging, cataloging, and discovery ensures stakeholders can access trusted and contextualized data assets seamlessly. These practices strengthen collaboration, reduce redundancy, and provide a governance structure for long-term success.

Designing data migrations

• Analyzing current stakeholder needs, users, processes, and technologies and creating a plan to get to desired state
• Planning migration to Google Cloud (e.g., BigQuery Data Transfer Service, Database Migration Service, Transfer Appliance, Google Cloud networking, Datastream)
• Designing the migration validation strategy
• Designing the project, dataset, and table architecture to ensure proper data governance

Summary: This section focuses on executing thoughtful data migrations. You will analyze stakeholder needs and the current technology landscape, then chart a path toward the ideal workload on Google Cloud. Tools such as BigQuery Data Transfer Service, Database Migration Service, Datastream, and cloud networking enhance migration planning and execution.

Design considerations such as dataset and table architecture are equally critical for ensuring governance and performance after migration. A strong validation strategy ensures minimal disruption, preserving confidence in the migration process while unlocking the benefits of Google Cloud.

Domain 2: Ingesting and processing the data (25% of the exam)

Planning the data pipelines

• Defining data sources and sinks
• Defining data transformation logic
• Networking fundamentals
• Data encryption

Summary: This part centers on building solid pipeline blueprints. You will evaluate sources and sinks, design transformation logic to make data usable, and plan encryption practices. Networking fundamentals support efficient and secure pipeline design, ensuring reliability across systems.

This strategic planning ensures that pipelines function properly within the constraints of infrastructure and compliance needs. By weaving in both security and performance considerations, you create the foundation that makes large-scale ingestion and processing seamless.

Building the pipelines

• Data cleansing
• Identifying the services (e.g., Dataflow, Apache Beam, Dataproc, Cloud Data Fusion, BigQuery, Pub/Sub, Apache Spark, Hadoop ecosystem, and Apache Kafka)
• Transformations Batch
• Transformations Streaming (e.g., windowing, late arriving data)
• Transformations Language
• Transformations Ad hoc data ingestion (one-time or automated pipeline)
• Data acquisition and import
• Integrating with new data sources

Summary: This section emphasizes hands-on pipeline development using a wide range of Google Cloud services and industry-standard tools. You will work with batch and streaming transformations, handle ad hoc ingestion, and integrate diverse data sources. Services like Dataflow, Pub/Sub, BigQuery, Spark, and Kafka are central for bringing data into action.

Equally valuable is mastering strategies for cleansing and transforming information. Whether preparing for real-time analytics, ETL processes, or complex data streams, this section highlights how scalable tools and transformations shape data into meaningful, ready-to-use states.

Deploying and operationalizing the pipelines

• Job automation and orchestration (e.g., Cloud Composer and Workflows)
• CI/CD (Continuous Integration and Continuous Deployment)

Summary: Here you will explore how to operationalize data pipelines so they are automated and reliable. Tools such as Cloud Composer and Workflows support orchestration while CI/CD practices ensure that pipelines can evolve smoothly with minimal manual intervention.

Building automation into deployment reduces operational effort while guaranteeing consistency across environments. This ensures pipelines stay up-to-date, resilient, and in line with shifting business needs while remaining streamlined and efficient to manage.

Domain 3: Storing the data (20% of the exam)

Selecting storage systems

• Analyzing data access patterns
• Choosing managed services (e.g., Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore)
• Planning for storage costs and performance
• Lifecycle management of data

Summary: This section focuses on evaluating and matching storage systems to organizational needs. By analyzing access patterns, you will choose from a variety of Google Cloud’s managed tools, from structured databases like Spanner and Cloud SQL to large-scale options like Bigtable and Cloud Storage.

Careful planning for cost and performance helps develop efficient and sustainable architectures. Lifecycle management mechanisms optimize usage over time, striking a balance between performance, availability, and value.

Planning for using a data warehouse

• Designing the data model
• Deciding the degree of data normalization
• Mapping business requirements
• Defining architecture to support data access patterns

Summary: This section dives into warehouse design. You will define a data model that aligns with business requirements and determine how normalized or denormalized data will be. This prepares data for large-scale analytics within systems such as BigQuery.

Design considerations for architecture directly affect performance and accessibility. Crafting models with these principles ensures users can quickly engage with data environments built for both flexibility and insights delivery.

Using a data lake

• Managing the lake (configuring data discovery, access, and cost controls)
• Processing data
• Monitoring the data lake

Summary: Here, the focus is on managing a data lake environment where unstructured and semi-structured data live alongside traditional data. You will learn to configure discovery, control access, and align costs effectively so that data lakes serve as scalable and efficient resources.

Establishing monitoring practices ensures secure and well-governed lakes. By combining governance with efficient processing, the result is a high-value environment that centralizes diverse datasets for comprehensive analytics.

Designing for a data mesh

• Building a data mesh based on requirements by using Google Cloud tools (e.g., Dataplex, Data Catalog, BigQuery, Cloud Storage)
• Segmenting data for distributed team usage
• Building a federated governance model for distributed data systems

Summary: This section introduces the concept of data meshes, emphasizing decentralized ownership and cross-team access to distributed data. Google Cloud’s tools such as Dataplex, Data Catalog, and BigQuery are highlighted as enablers of a modern, federated structure.

By segmenting and governing data effectively, organizations empower teams to self-serve with trusted data while maintaining consistency and oversight. A federated governance model strengthens collaboration and innovation, helping scale analytics across distributed environments.

Domain 4: Preparing and using data for analysis (15% of the exam)

Preparing data for visualization

• Connecting to tools
• Precalculating fields
• BigQuery materialized views (view logic)
• Determining granularity of time data
• Troubleshooting poor performing queries
• Identity and Access Management (IAM) and Cloud Data Loss Prevention (Cloud DLP)

Summary: This section ensures data is prepared for effective visualization and analytical use. You will design materialized views, create precalculated fields, and determine appropriate levels of data granularity for analysis. Managing access and applying Cloud DLP further secures sensitive information while making data prepared for broad-based usage.

Optimizing queries ensures that reports and dashboards remain efficient, reliable, and tailored for decision-makers. These practices help bring efficiency to business intelligence workflows and elevate data consumption experiences.

Sharing data

• Defining rules to share data
• Publishing datasets
• Publishing reports and visualizations
• Analytics Hub

Summary: Sharing fuels collaboration, and this section highlights how datasets, reports, and visualizations are published securely to different stakeholders. Analytics Hub serves as a central distribution point for datasets, supporting visibility and access across teams.

Developing rules ensures these resources are both accessible and secure. This approach builds a culture where collaboration with trusted, high-quality data becomes a seamless extension of everyday workflows.

Exploring and analyzing data

• Preparing data for feature engineering (training and serving machine learning models)
• Conducting data discovery

Summary: This section focuses on preparing data for advanced data science workflows. By organizing data for feature engineering, you ensure readiness for training, serving, and applying machine learning models at scale.

You will also conduct data discovery processes to reveal opportunities in large datasets. These practices align analytics and machine learning, making it possible to turn insights into intelligent, automated solutions.

Domain 5: Maintaining and automating data workloads (18% of the exam)

Optimizing resources

• Minimizing costs per required business need for data
• Ensuring that enough resources are available for business-critical data processes
• Deciding between persistent or job-based data clusters (e.g., Dataproc)

Summary: This section highlights strategies for balancing costs with performance to match business needs. You will explore how to minimize expenses while making sure critical data processes are always provided with sufficient resources.

You will also compare persistent versus job-based clusters in Dataproc, enabling data engineers to optimize deployments for cost-efficiency and workload demands. These strategies ensure every investment delivers tangible business value.

Designing automation and repeatability

• Creating directed acyclic graphs (DAGs) for Cloud Composer
• Scheduling jobs in a repeatable way

Summary: This section emphasizes automation’s role in ensuring smooth operations. You will work with Cloud Composer to implement DAGs and automate scheduling, allowing repetitive processes to run consistently and dependably.

By reducing manual interventions, automation introduces efficiency while making processes more reliable. This ensures data workloads scale successfully as environments expand.

Organizing workloads based on business requirements

• Flex, on-demand, and flat rate slot pricing (index on flexibility or fixed capacity)
• Interactive or batch query jobs

Summary: This section covers how to organize and align workloads with business requirements. You will explore options for slot pricing, balancing flexibility and predictable capacity while choosing between interactive and batch workloads.

Developing cost-aware structures supports decision-making in rapidly changing environments. These practices exemplify how workload planning strengthens alignment with business needs.

Monitoring and troubleshooting processes

• Observability of data processes (e.g., Cloud Monitoring, Cloud Logging, BigQuery admin panel)
• Monitoring planned usage
• Troubleshooting error messages, billing issues, and quotas
• Manage workloads, such as jobs, queries, and compute capacity (reservations)

Summary: Here you will focus on observing and maintaining visibility over systems. Monitoring tools such as Cloud Monitoring and Logging establish transparency over system health, performance, and usage.

Coupled with troubleshooting strategies, this ensures production workloads remain resilient, functional, and cost-effective. From billing issues to compute capacity adjustments, monitoring guarantees continuous operational excellence.

Maintaining awareness of failures and mitigating impact

• Designing system for fault tolerance and managing restarts
• Running jobs in multiple regions or zones
• Preparing for data corruption and missing data
• Data replication and failover (e.g., Cloud SQL, Redis clusters)

Summary: This section emphasizes building fault-tolerant systems that prepare for issues before they occur. You will learn to design jobs that span multiple regions or zones and employ replication to ensure continuity.

Failover strategies and approaches for handling corruption or missing data reinforce resilience. Together, these practices ensure workloads overcome disruptions with minimal impact to business operations.