GCP Professional Data Engineer

Reviewing core GCP services for data engineering.

• GCP Project structure, IAM, and Security.
• Virtual Private Cloud (VPC) networking for data services.
• Cloud Storage (GCS) as a data lake.
• Cloud SQL and Cloud Spanner for relational data.

Mastering Google's serverless data warehouse.

• BigQuery architecture, datasets, and tables.
• Loading data (batch and streaming).
• Querying with advanced SQL (window functions, UDFs).
• Data modeling: Partitioning and Clustering for performance.

Managing cost, performance, and security.

• BigQuery pricing models (On-demand vs. Flat-rate).
• Query optimization and cost control best practices.
• BigQuery BI Engine.
• Securing BigQuery: Column-level and row-level security.

Building a scalable, global messaging service.

• Pub/Sub architecture (Topics, Subscriptions, Publishers, Subscribers).
• Message ordering and delivery guarantees.
• Dead-letter queues and error handling.
• Schema management with Pub/Sub schemas.

Serverless batch processing with Apache Beam.

• The Apache Beam programming model (PCollection, PTransform).
• Writing Dataflow pipelines in Python.
• Common transforms (ParDo, GroupByKey).
• Dataflow templates for reusable pipelines.

Handling real-time data streams with Apache Beam.

• Windowing (Tumbling, Sliding, Session).
• Triggers and watermarks for handling late data.
• Building a Pub/Sub to BigQuery streaming pipeline.
• Monitoring and troubleshooting streaming jobs.

Using managed Hadoop and Spark clusters.

• Dataproc vs. Dataflow: When to use which.
• Creating and managing Dataproc clusters.
• Submitting Spark and PySpark jobs.
• Autoscaling and component gateways.

Managing complex dependencies with managed Apache Airflow.

• Cloud Composer architecture.
• Writing DAGs (Directed Acyclic Graphs) in Python.
• Using GCP operators (BigQuery, Dataflow, Dataproc).
• Monitoring and debugging workflows in the Airflow UI.

Understanding the data engineer's role in ML.

• ML concepts for data engineers.
• Feature Engineering at scale.
• Building and training models with BigQuery ML (BQML).
• Exporting models for prediction.

Building production-grade MLOps pipelines.

• Vertex AI overview (Notebooks, Pipelines, Endpoints).
• Building MLOps workflows with Vertex AI Pipelines.
• Deploying models to Vertex AI Endpoints for serving.
• Model monitoring for drift and skew.

Ensuring pipelines are secure, observable, and governed.

• Securing data with Cloud KMS and VPC Service Controls.
• Data Governance with Data Catalog and Data Loss Prevention (DLP).
• Monitoring pipelines with Cloud Monitoring and Cloud Logging.
• Troubleshooting and optimizing data pipelines.

Final review and exam strategies.

• Review of the core exam domains.
• Analyzing the official case studies (e.g., Looker, J.P. Morgan).
• Full-Length Practice Exam Simulation.
• Review of Practice Exam & Final Q&A.