Google Professional Data Engineer
Course Bootcamp
The Professional Data Engineer Bootcamp equips professionals with the skills to design, build, secure, monitor, and optimize modern data platforms on Google Cloud. Through hands-on labs and real-world projects, participants learn how to create scalable data pipelines, implement data governance, support analytics and AI initiatives, and manage enterprise-grade data solutions that drive informed business decision-making.
Duration
36 hours
Session Days
Weekdays
Weekends
Course Delivery
Classroom
Live Remote
Certification
Google Professional Machine Learning Engineer
Google Professional Exam
Cost
USD 199
* Discounts available
Bootcamp Features
Why to become Google Professional Data Engineer?
Validate Cloud Data Expertise
Demonstrate your ability to design, build, secure, and optimize enterprise-scale data solutions on Google Cloud.
Gain Global Recognition
Google Cloud certifications are respected worldwide and provide credibility in competitive technology and data-driven industries.
High Industry Demand
Organizations increasingly rely on skilled data engineers to support analytics, artificial intelligence, and digital transformation initiatives.
Master End-to-End Data Lifecycle
Gain expertise from data ingestion and storage through governance, analytics, automation, and optimization.
Build Expertise in Data Engineering & Analytics
Develop practical skills using Data Engineering practices and Data Analytics Tool.
Increase Professional Value
Certified data engineers are highly sought after due to their ability to create reliable business-critical data systems.
Accelerated Career Growth
Open opportunities for advanced positions in data engineering, cloud architecture, analytics, and platform engineering.
Future-Proof Your Career
Learn modern technologies powering large-scale data platforms, AI systems, and business intelligence solutions.
What Skills Will You Learn?
Who is this Course For?
Curriculum
▸ Identity and Access Management (e.g., Cloud IAM and organization policies)
▸ Data security (encryption and key management)
▸ Privacy (e.g., strategies to handle personally identifiable information)
▸ Regional considerations (data sovereignty) for data access and storage
▸ Legal and regulatory compliance
▸ Designing the project, dataset, and table architecture to ensure proper data governance
▸ Multi-environment use cases (development vs. production)
Designing for reliability and fidelity
▸ Preparing and cleaning data (e.g., Dataform, Dataflow, and Cloud Data Fusion, prompting LLMs for query generation)
▸ 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
Designing for flexibility and portability
▸ Mapping current and future business requirements to the architecture 1
▸ Designing for data and application portability (e.g., multi-cloud and data residency requirements)
▸ Data staging, cataloging, profiling, and discovery (data governance)
Designing data migrations
▸ Analyzing current stakeholder needs, users, processes, and technologies, and creating a plan to get to desired state
▸ Planning migration and validation to Google Cloud (e.g., BigQuery Data Transfer Service, Database Migration Service, Transfer Appliance, Google Cloud networking, Datastream)
▸ Defining data sources and sinks
▸ Defining data transformation and orchestration logic
▸ Networking fundamentals
▸ Data encryption
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, Streaming, Processing logic, AI data enrichment
▸ Data acquisition and import
▸ Integrating with new data sources
Deploying and operationalizing the pipelines
▸ Job automation and orchestration (e.g., Cloud Composer and Workflows)
▸ CI/CD (Continuous Integration and Continuous Deployment)
▸ Analyzing data access patterns
▸ Choosing managed services (e.g., BigQuery, BigLake, AlloyDB, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore)
▸ Planning for storage costs and performance
▸ Lifecycle management of data
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
Using a data lake
▸ Managing the lake (configuring data discovery, access, and cost controls)
▸ Processing data
▸ Monitoring the data lake
Designing for a data platform
▸ Building a data platform based on requirements by using Google Cloud tools (e.g., Dataplex, Dataplex Catalog, BigQuery, Cloud Storage)
▸ Building a federated governance model for distributed data systems
▸ Connecting to tools
▸ Precalculating fields
▸ BigQuery features for business intelligence (e.g., BI Engine, materialized views)
▸ Troubleshooting poor performing queries
▸ Security, data masking, Identity and Access Management (IAM), and Cloud Data Loss Prevention (Cloud DLP)
Preparing data for AI and ML
▸ Preparing data for feature engineering, training and serving machine learning models (e.g., BigQueryML)
▸ Preparing unstructured data for embeddings and retrieval-augmented generation (RAG)
Sharing data
▸ Defining rules to share data
▸ Publishing datasets
▸ Publishing reports and visualizations
▸ BigQuery sharing (Analytics Hub)
▸ 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)
Designing automation and repeatability
▸ Creating directed acyclic graphs (DAGs) for Cloud Composer
▸ Scheduling and orchestrating jobs in a repeatable way
Organizing workloads based on business requirements
▸ Capacity management (e.g., BigQuery Editions and reservations)
▸ Interactive or batch query jobs
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)
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) 5