Course Introduction
Google Cloud Data Engineering with Dataflow and Dataproc Training Course provides a comprehensive, practical, and industry-focused learning experience for professionals who want to design, build, deploy, and manage modern data engineering solutions on Google Cloud. The course develops essential skills in data ingestion, transformation, processing, orchestration, analytics, and scalable cloud data infrastructure.
Participants explore how Google Cloud services can be combined to create reliable and scalable data pipelines for batch and streaming workloads. Particular attention is given to Google Cloud Dataflow and Dataproc, enabling learners to understand when and how to use Apache Beam, Apache Spark, Hadoop technologies, and managed cloud services for demanding enterprise data engineering environments.
The course takes learners through the complete data engineering lifecycle, from collecting raw data and designing storage architectures to transforming information, implementing pipelines, monitoring workloads, and optimizing processing performance. Practical scenarios help participants understand how data engineering principles translate into solutions for real-world business intelligence, analytics, machine learning, and operational requirements.
Emerging data engineering practices are also integrated throughout the program, including real-time analytics, event-driven architectures, cloud-native processing, data quality management, observability, cost optimization, security, and responsible data handling. Participants gain exposure to modern approaches for building resilient pipelines that can accommodate increasing data volumes, diverse data formats, and rapidly changing organizational requirements.
The training also emphasizes practical decision-making around Dataflow and Dataproc by examining their capabilities, architectural patterns, performance considerations, integration options, and appropriate use cases. Learners develop the ability to evaluate workload requirements and select suitable processing technologies rather than relying on a single platform or processing approach for every data engineering challenge.
By completing this program, participants will be better prepared to contribute to cloud data engineering initiatives, modernize legacy data workflows, and develop scalable data processing solutions on Google Cloud. The combination of conceptual knowledge, architecture considerations, implementation techniques, troubleshooting practices, and emerging industry topics makes the course suitable for professionals seeking practical expertise in contemporary cloud data engineering.
Duration
10 days
Who Should Attend
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Data engineers responsible for developing and maintaining enterprise data pipelines.
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Cloud engineers who need practical experience with Google Cloud data processing services.
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Big data professionals working with Apache Beam, Apache Spark, Hadoop, or distributed processing.
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Data architects designing scalable cloud-based data platforms and processing architectures.
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ETL developers seeking to modernize traditional batch processing workflows in the cloud.
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Analytics engineers responsible for preparing reliable datasets for business intelligence initiatives.
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DevOps and platform engineers supporting deployment, monitoring, and operations of data workloads.
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Database professionals transitioning from traditional data platforms to cloud data engineering environments.
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Machine learning engineers who require dependable pipelines for preparing large-scale training datasets.
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Business intelligence professionals who want stronger technical understanding of cloud data pipelines.
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Software engineers developing applications that generate, consume, or process high-volume data streams.
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Technical leads and project managers overseeing Google Cloud data modernization and transformation projects.
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Data scientists who need scalable and production-ready methods for data preparation and processing.
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IT professionals preparing for responsibilities involving Google Cloud analytics and data engineering services.
Course Objectives
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Develop a strong understanding of modern cloud data engineering principles and how they apply to scalable Google Cloud architectures.
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Design robust batch and streaming data pipelines using Google Cloud Dataflow and appropriate Apache Beam processing patterns.
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Configure and operate Dataproc clusters for distributed data processing using Apache Spark and related ecosystem technologies.
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Select suitable data processing technologies by evaluating workload characteristics, latency requirements, scalability needs, and operational constraints.
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Build reliable data ingestion workflows capable of processing structured, semi-structured, and unstructured information from multiple sources.
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Apply data transformation, cleansing, enrichment, validation, and aggregation techniques to improve downstream data usability and quality.
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Implement real-time streaming pipelines that process continuously generated events while supporting reliability, scalability, and low-latency analytics.
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Integrate data processing workloads with Google Cloud storage, databases, messaging platforms, analytics services, and other cloud-native technologies.
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Monitor pipeline performance, diagnose failures, identify bottlenecks, and apply practical troubleshooting techniques across distributed data workloads.
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Apply security, access control, encryption, governance, and responsible data management principles throughout the data engineering lifecycle.
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Optimize cloud data workloads for performance and cost by evaluating resource allocation, processing strategies, storage patterns, and execution configurations.
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Apply modern data engineering practices involving observability, automation, orchestration, data quality, resilience, and operational readiness for production environments.
Comprehensive Course Outline
Module 1: Foundations of Google Cloud Data Engineering
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Core principles of modern data engineering, cloud architectures, distributed processing, and enterprise data lifecycle management.
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Google Cloud data engineering ecosystem, major services, architectural roles, integration patterns, and common enterprise use cases.
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Data engineering workload characteristics, including batch processing, streaming analytics, latency requirements, scalability, and reliability considerations.
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Designing cloud-native data platforms that balance performance, maintainability, security, operational simplicity, and long-term business requirements.
Module 2: Google Cloud Storage and Data Lake Foundations
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Designing scalable data lake architectures using Google Cloud Storage for structured, semi-structured, and unstructured enterprise datasets.
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Storage organization strategies involving buckets, objects, naming conventions, lifecycle policies, retention requirements, and data access patterns.
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Data lake ingestion considerations covering file formats, partitioning strategies, compression approaches, metadata management, and downstream processing.
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Emerging data lakehouse concepts and their implications for governance, analytics, interoperability, performance, and modern cloud data architectures.
Module 3: Data Ingestion and Messaging with Pub/Sub
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Designing reliable data ingestion architectures using Google Cloud Pub/Sub for asynchronous, event-driven, and high-volume data workloads.
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Topics, subscriptions, message delivery, acknowledgements, retention, ordering, and practical patterns for dependable event processing.
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Integrating Pub/Sub with Dataflow pipelines to support continuous ingestion, transformation, enrichment, and real-time analytical processing.
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Managing ingestion challenges such as duplicate messages, late-arriving events, malformed records, backpressure, retries, and operational failures.
Module 4: Apache Beam and Dataflow Fundamentals
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Introduction to Apache Beam programming concepts, pipelines, transforms, PCollections, runners, and portable data processing architectures.
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Building Dataflow pipelines for scalable batch processing while applying suitable transformations, parallelization, and execution strategies.
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Understanding Dataflow execution architecture, worker management, autoscaling behavior, resource allocation, and operational considerations.
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Developing reusable pipeline components that support maintainability, testing, deployment consistency, and production-oriented engineering practices.
Module 5: Advanced Dataflow Batch Processing
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Implementing advanced batch transformations for filtering, joining, grouping, aggregating, enriching, and restructuring large enterprise datasets.
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Applying partitioning, parallel processing, worker configuration, and pipeline optimization techniques to improve large-scale batch execution.
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Handling schema variations, malformed records, missing values, data validation, and exception scenarios within production data processing pipelines.
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Designing batch workflows that support repeatability, recoverability, auditability, operational monitoring, and reliable downstream data delivery.
Module 6: Dataflow Streaming and Real-Time Processing
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Building real-time Dataflow pipelines that process continuously arriving events while maintaining scalable and resilient streaming architectures.
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Understanding event time, processing time, windows, triggers, watermarks, and late-data handling for accurate real-time analytical results.
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Implementing streaming transformations that support aggregations, enrichment, filtering, joins, deduplication, and continuous data quality validation.
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Addressing emerging real-time analytics requirements involving low-latency decisions, operational intelligence, event-driven applications, and streaming dashboards.
Module 7: Dataproc and Apache Spark Architecture
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Understanding Dataproc architecture, managed cluster concepts, Apache Spark integration, Hadoop ecosystem components, and cloud-based distributed processing.
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Creating and configuring Dataproc clusters according to workload requirements, processing patterns, security expectations, and operational constraints.
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Developing Spark-based data processing workflows for large datasets while applying distributed computing principles and efficient transformation strategies.
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Comparing managed Spark processing with alternative cloud processing approaches to identify suitable architectures for different enterprise workloads.
Module 8: Advanced Dataproc and Spark Workloads
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Designing advanced Spark jobs involving joins, aggregations, partitioning, caching, serialization, and distributed execution across large datasets.
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Optimizing Spark applications through appropriate cluster sizing, executor configuration, partition management, and workload-specific performance techniques.
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Processing large-scale files and datasets using Spark while considering storage formats, schema evolution, data locality, and computational efficiency.
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Implementing reliable Spark workflows with logging, monitoring, error handling, testing, deployment practices, and operational recovery procedures.
Module 9: Data Transformation, Quality, and Governance
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Applying systematic data cleansing, validation, standardization, enrichment, deduplication, and transformation practices across cloud pipelines.
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Establishing data quality controls that identify completeness, accuracy, consistency, timeliness, validity, and uniqueness issues before analytical consumption.
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Implementing metadata, lineage, classification, ownership, and governance practices that improve trust and accountability across enterprise data assets.
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Addressing emerging data governance challenges involving rapidly changing schemas, distributed data platforms, regulatory expectations, and responsible data usage.
Module 10: BigQuery Integration and Analytical Data Processing
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Integrating Dataflow and Dataproc workloads with BigQuery to create scalable pipelines for analytical storage, querying, and business intelligence.
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Designing efficient data loading and transformation strategies that support analytical workloads while reducing unnecessary processing and storage overhead.
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Applying partitioning, clustering, schema design, query optimization, and workload management principles for high-volume analytical datasets.
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Building end-to-end architectures that connect operational sources, processing engines, analytical storage, dashboards, and downstream data consumers.
Module 11: Pipeline Orchestration and Automation
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Designing automated data workflows that coordinate ingestion, processing, validation, storage, transformation, and analytical delivery across multiple services.
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Applying orchestration principles for dependencies, scheduling, retries, error handling, notifications, and recovery across complex data pipelines.
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Integrating data engineering workflows with automation and deployment practices to improve repeatability, reliability, governance, and operational consistency.
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Exploring emerging approaches to pipeline automation, event-driven orchestration, infrastructure management, and intelligent operational workflows.
Module 12: Data Engineering Security and Governance
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Implementing Google Cloud Identity and Access Management principles to control access to data, processing services, storage resources, and pipeline operations.
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Applying encryption, secure connectivity, service accounts, least-privilege access, and credential management practices across data engineering environments.
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Designing secure data pipelines that protect sensitive information while supporting legitimate processing, analytics, monitoring, and organizational access requirements.
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Examining emerging security challenges involving cloud data platforms, distributed workloads, third-party integrations, privacy requirements, and expanding attack surfaces.
Module 13: Monitoring, Observability, and Troubleshooting
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Establishing effective monitoring practices for Dataflow, Dataproc, Spark applications, ingestion systems, storage services, and analytical workloads.
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Using logs, metrics, alerts, execution information, and operational dashboards to identify pipeline failures and performance degradation.
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Troubleshooting common distributed processing problems involving worker failures, memory constraints, slow transformations, skewed workloads, and connectivity issues.
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Developing observability strategies that provide meaningful operational visibility while supporting proactive incident detection, diagnosis, and continuous improvement.
Module 14: Performance and Cost Optimization
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Evaluating Dataflow and Dataproc resource utilization to identify opportunities for improving processing speed, scalability, reliability, and cloud cost efficiency.
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Applying optimization techniques involving worker configuration, autoscaling, partitioning, caching, parallelism, data formats, and processing architecture.
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Designing cost-aware data pipelines that balance business service levels, processing requirements, storage consumption, and infrastructure expenditure.
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Addressing emerging FinOps considerations for data engineering, including workload governance, resource accountability, optimization automation, and sustainable cloud operations.
Module 15: Advanced and Emerging Data Engineering Practices
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Exploring data mesh, data fabric, lakehouse architectures, and decentralized data ownership models within modern enterprise data environments.
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Examining real-time analytics, streaming-first architectures, event-driven systems, and operational data processing for rapidly changing business requirements.
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Understanding the growing role of artificial intelligence and machine learning in data pipeline automation, data quality, anomaly detection, and engineering productivity.
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Investigating emerging challenges involving generative AI data workloads, vector data, multimodal information, responsible AI, governance, and scalable data preparation.
Module 16: Capstone Architecture, Deployment, and Production Readiness
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Designing an end-to-end Google Cloud data engineering solution that integrates ingestion, processing, storage, analytics, monitoring, security, and governance.
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Developing production-ready Dataflow and Dataproc workflows that demonstrate scalability, resilience, observability, maintainability, and operational readiness.
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Evaluating architectural decisions through workload requirements, performance expectations, security considerations, reliability objectives, and cost management principles.
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Presenting and reviewing a practical data engineering architecture while identifying opportunities for modernization, optimization, automation, and future scalability.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
