Google Cloud Professional Data Engineer (PDE) Certification in Qatar

1.1 Designing for security and compliance. Considerations include: 

• 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

1.2 Designing for reliability and fidelity. Considerations include:

• 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

1.3 Designing for flexibility and portability. Considerations include:

• 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)

1.4 Designing data migrations. Considerations include:

• 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 

2.1 Planning the data pipelines. Considerations include:

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

2.2 Building the pipelines. Considerations include:

• 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
• Streaming (e.g., windowing, late arriving data)
• Language
• Ad hoc data ingestion (one-time or automated pipeline)
• Data acquisition and import
• Integrating with new data sources 

2.3 Deploying and operationalizing the pipelines. Considerations include:

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

3.1 Selecting storage systems. Considerations include:

• 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

3.2 Planning for using a data warehouse. Considerations include:

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

3.3 Using a data lake. Considerations include:

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

3.4 Designing for a data mesh. Considerations include:

• 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

4.1 Preparing data for visualization. Considerations include:

• 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)

4.2 Sharing data. Considerations include:

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

4.3 Exploring and analyzing data. Considerations include:

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

5.1 Optimizing resources. Considerations include:

• 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)

5.2 Designing automation and repeatability. Considerations include:

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

5.3 Organizing workloads based on business requirements. Considerations include:

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

5.4 Monitoring and troubleshooting processes. Considerations include:

• 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)

5.5 Maintaining awareness of failures and mitigating impact. Considerations include:

• 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)