Course Overview of Managing a Data Mesh with Dataplex

Dataplex is an intelligent data fabric designed for organizations to manage distributed data at scale. This course provides a deep dive into using Dataplex to build a “Data Mesh”—a modern architecture that decentralizes data ownership. Through guided lectures and hands-on exercises, students will learn to organize data into lakes and zones, automate metadata discovery, and enforce unified security and data quality across the enterprise.

After completing Managing a Data Mesh with Dataplex, students will be able to:

  • Explain the principles of a modern data platform and data mesh.
  • Set up and configure Dataplex environments (Lakes, Zones, and Assets).
  • Secure distributed data using unified IAM roles and metadata security.
  • Automate data standardization and preparation using Dataplex tasks.
  • Implement data tagging and search capabilities via Data Catalog.
  • Design and execute data quality processes and report on the findings.
  • Apply Google-recommended best practices for managing data fabrics.

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Key Features of Managing a Data Mesh with Dataplex

  •  Data Mesh Architecture: Focuses on shifting from centralized data silos to a decentralized, domain-led data mesh.

  • Unified Governance: Learn to manage data across different storage types like Cloud Storage and BigQuery from a single pane of glass.

  • Automated Data Discovery: Master the automation of metadata harvesting and asset discovery.

  • Scale-Out Security: Implement unified security policies across lakes, zones, and assets.

  • Data Quality & Profiling: Integrated modules on AutoDQ (Auto Data Quality) and data profiling to ensure data reliability.

  • Practical Learning: Features 7 modules and 6 intensive hands-on labs, including a final Challenge Lab. 

Who should Attend Managing a Data Mesh with Dataplex?

  • Data Engineers
  • Data Architects
  • Customers looking to implement data governance.

Prerequisites of Managing a Data Mesh with Dataplex

  • Completion of "Modernizing Data Lakes and Data Warehouses with Google Cloud".
  • Completion of "Building Batch Data Pipelines on Google Cloud" (Data Engineer path) or equivalent experience.

    • Why choose CloudThat as your training partner for Managing a Data Mesh with Dataplex?

    • Specialized GCP Focus: CloudThat specializes in cloud technologies, offering focused and specialized training programs. We are Authorized Trainers for the Google Cloud Platform. This specialization ensures in-depth coverage of GCP services, use cases, best practices, and hands-on experience tailored specifically for GCP. 
    • Industry-Recognized Trainers: CloudThat has a strong pool of industry-recognized trainers certified by GCP. These trainers bring real-world experience and practical insights into the training sessions, comprehensively understanding how GCP is applied in different industries and scenarios. 
    • Hands-On Learning Approach: CloudThat emphasizes a hands-on learning approach. Learners can access practical labs, real-world projects, and case studies that simulate actual GCP environments. This approach allows learners to apply theoretical knowledge in practical scenarios, enhancing their understanding and skill set. 
    • Customized Learning Paths: CloudThat understands that learners have different levels of expertise and varied learning objectives. We offer customized learning paths, catering to beginners, intermediate learners, and professionals seeking advanced GCP skills. 
    • Interactive Learning Experience: CloudThat's training programs are designed to be interactive and engaging. We utilize various teaching methodologies like live sessions, group discussions, quizzes, and mentorship to keep learners engaged and motivated throughout the course. 
    • Placement Assistance and Career Support: CloudThat often provides placement assistance and career support services. This includes resume building, interview preparation, and connecting learners with job opportunities through our network of industry partners and companies looking for GCP-certified professionals. 
    • Continuous Learning and Updates: CloudThat ensures that our course content is regularly updated to reflect the latest trends, updates, and best practices within the GCP ecosystem. This commitment to keeping the content current enables learners to stay ahead in their GCP knowledge. 
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    Learning Objectives of Managing a Data Mesh with Dataplex

    • Configuration: Successfully configure the Dataplex hierarchy of lakes and zones.
    • Data Processing: Use Dataplex tasks to run data preparation and standardization. 
    • Security & Policy: Manage IAM permissions and policy management at the data lake level. 
    • Metadata Management: Understand the difference between technical and business metadata and implement data lineage.
    • Quality Assurance: Design and execute data quality profiling in BigQuery.
    • Best Practices: Implement a production-ready data mesh using proven design patterns.

    Course Outline for Managing a Data Mesh with Dataplex Download Course Outline

    Lecture Content

    • Core principles of modern data platforms and distributed architectures
    • Shifting from centralized silos to a decentralized, domain-led data mesh
    • Overview of Dataplex features, core components, and intelligent fabric capabilities

    Learning Objectives

    • Identify the importance of a modern data platform and data mesh architecture

    Lecture Content

    • Designing the logical layout of a data fabric
    • Defining and setting up Dataplex Lakes as top-level domain containers
    • Configuring Raw and Curated Zones to organize data readiness
    • Attaching physical storage data assets (Cloud Storage buckets and BigQuery datasets)

    Learning Objectives

    • Configure and set up the Dataplex hierarchy of lakes, zones, and assets

    Lab Content

    • Organizing Data into Lakes and Zones

    Lecture Content

    • Overview of internal data processing options and execution engines in Dataplex
    • Building automated data preparation workflows
    • Establishing data standardization and transformation rules using Dataplex tasks

    Learning Objectives

    • Process data and manage execution environments using Dataplex tasks
    • Configure and run data preparation and data standardization tasks

    Lab Content

    • Standardize Data using Dataplex Tasks

    Lecture Content

    • Centralized identity and access management (IAM) permissions and roles
    • Establishing scale-out security policies across distributed data lakes
    • Policy management, inheritance, and row/column security patterns
    • Securing data assets and mapping metadata abstraction layers

    Learning Objectives

    • Secure data lakes, zones, and assets in Dataplex using uniform security policies

    Lab Content

    • Manage Data Security using Dataplex

    Lecture Content

    • Introduction to Data Catalog architectural integration
    • Differentiating between technical metadata and business metadata
    • Creating tags and reusable tag templates for data governance
    • Discovering assets using entries, entry groups, and full-text search
    • Tracking and auditing data lifecycle journeys through automated data lineage

    Learning Objectives

    • Implement tagging for resources and use tags to search for assets
    • Document and visualize data assets using technical and business metadata

    Lab Content

    • Data Catalog and Data Lineage

    Lecture Content

    • Defining data validation rule structures with data quality tasks and AutoDQ
    • Automating data profiling to identify anomalies and trends
    • Generating and reporting on overall data health in BigQuery

    Learning Objectives

    • Design, execute, and report on automated data quality and data profiling processes

    Lab Content

    • Lab Content

    Lecture Content

    • Proven enterprise architecture patterns for production data mesh environments
    • Governance strategies, optimization tips, and common pitfalls to avoid
    • Comprehensive end-to-end multi-domain deployment demo

    Learning Objectives

    • Implement Google-recommended best practices and design patterns for Dataplex fabrics

    Lab Content

    • Managing a Data Mesh with Dataplex

    Certification Details of Managing a Data Mesh with Dataplex

      CloudThat Course Completion Certificate will be awarded to all learners who complete the training.

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    FAQs for Managing a Data Mesh with Dataplex

    It is an architectural pattern that decentralizes data ownership among domain owners, which is the core focus of this course.

    The course explains this hierarchy in Module 02, where Lakes are top-level containers and Zones organize assets by readiness or type.

    Yes, Dataplex manages assets in both Cloud Storage and BigQuery, and Module 06 specifically uses BigQuery for profiling.

    The course includes several hands-on labs and concludes with a "Challenge Lab" to test your overall competency.

    It provides unified security management across all assets within a lake, as covered in Module 04.

    This stands for Automatic Data Quality, a feature taught in Module 06 to help report on data health.

    Yes, Module 05 covers Data Catalog, including tagging, entry groups, and data lineage.

    While Dataflow is a prerequisite skill, Dataplex uses its own "Tasks" for data processing, which are covered here.

    This is a 2-day instructor-led program.

    Yes, the course teaches you to manage both technical and business metadata using tags and templates.

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